Associations Between Event-Based (Mis)Recognition by STEM Authorities with STEM Identity and STEM Career Aspirations

Amdad Ahmed Awsaf; Gerhard Sonnert; Philip Sadler; Remy Dou

arxiv: 2607.06043 · v1 · pith:2TNGWJCA · submitted 2026-07-07 · physics.ed-ph

Associations Between Event-Based (Mis)Recognition by STEM Authorities with STEM Identity and STEM Career Aspirations

Amdad Ahmed Awsaf , Remy Dou , Gerhard Sonnert , Philip Sadler This is my paper

Reviewed by Pith T0 review T1 audit T2 compute T3 formal T4 kernel 2026-07-08 18:12 UTCglm-5.2pith:2TNGWJCA record.json open to challenge →

classification physics.ed-ph PACS 01.40.Fk01.40.J-

keywords stemidentitycareeraspirationsindividualsminoritizedalthoughalways

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The pith

Being called on in class predicts STEM career hopes

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper examines whether specific moments of recognition by teachers and other adults during K-12 STEM education predict whether minoritized college students come to see themselves as STEM people and aspire to STEM careers. Surveying 1,134 students at Minority Serving Institutions, the authors find that three concrete experiences—being called on to answer questions in class, being recommended for an advanced STEM course, and receiving awards in STEM activities—significantly predict both STEM identity and career aspirations. Notably, misrecognition experiences (such as being passed over for recommendation or having negative classroom experiences) showed no significant association with either outcome. The authors argue that these findings suggest interventions should focus on engineering specific positive recognition moments rather than merely avoiding negative ones.

Core claim

The central finding is a dissociation between recognition and misrecognition: specific positive recognition events (being called on, recommended, awarded) predict STEM identity and career aspirations among minoritized students, while misrecognition events do not significantly predict either outcome. This challenges the assumption that negative experiences function as symmetrical impediments to positive ones. The study also finds that not all positive recognition is equal—being invited to competitions and being accepted into selective programs did not reach significance, whereas the three teacher-mediated classroom experiences did.

What carries the argument

The study uses principal components analysis to separate recognition experiences from misrecognition experiences into distinct factors, then applies logistic regression (for career aspiration) and multiple linear regression (for STEM identity) to test each factor's predictive power. The central analytical object is the distinction between event-based recognition and event-based misrecognition as independent predictors.

If this is right

Teacher training programs could be evaluated on whether they increase the frequency of specific recognition behaviors (calling on students, recommending for advanced courses) rather than generic encouragement
The asymmetry between recognition and misrecognition effects suggests that reducing negative experiences alone may be insufficient to boost STEM participation—active positive intervention is needed
The finding that competition invitations and selective program acceptance were not significant predictors could redirect intervention design away from competitive structures toward everyday classroom interactions
If the three significant recognition events are causal rather than merely correlational, they represent low-cost, scalable intervention targets that require no new programs or curricula

Load-bearing premise

The study asks college students to recall whether they were 'often called upon,' 'recommended by teachers,' or 'received awards' during their K-12 years, and treats these recollections as accurate records of past experiences. If students who already strongly identify as STEM people systematically remember more recognition than they actually received, the reported associations could reflect memory bias rather than genuine links between past events and current outcomes.

What would settle it

If a longitudinal study tracking recognition events in real time (rather than retrospectively) found no association between these specific events and later STEM identity or career aspirations, the core claim would be undermined.

read the original abstract

Several programs for marginalized young people have been intentionally designed to increase the diversity in STEM careers; however, the participation of racially and ethnically minoritized individuals continued to be underrepresented in STEM disciplines. Using discipline-based STEM identity theories and drawing data from an NSF-funded survey of 1,134 participants, these regression analyses examine the impact of prior educational experiences on STEM identity (i.e., seeing oneself as a STEM person) and career aspirations among individuals attending Minority Serving Institutions (MSIs). The study's findings indicate that although positive reinforcement positively correlates with the STEM identity construct, the efforts are not always supportive enough to predict STEM career aspirations for minoritized individuals. It underscores the importance of explicitly designing appropriate interventions to support STEM identity formation and STEM career pursuit.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

Conference paper with a genuinely interesting null result on misrecognition, but the analysis is preliminary — 31% listwise deletion and low PCA variance explained mean the current estimates are fragile.

read the letter

Here's the short version: this is a NARST conference paper reporting that specific recognition experiences (being called on, recommended for advanced classes, receiving awards) predict STEM identity and career aspirations in an MSI sample, while misrecognition experiences do not. The null on misrecognition is the most interesting finding because it cuts against prior work (Chachashvili-Bolotin et al., 2016; Simpson & Maltese, 2016). That contrast is a genuine new data point worth disseminating. The authors deserve credit for flagging their own limitations — they explicitly label findings as preliminary and plan multiple imputation. That honesty matters and is appropriately placed. The framework (Hazari et al., 2010; Dou & Cian, 2022) is well-established and the survey items are grounded in prior literature with expert review. The regression diagnostics are standard and the authors checked assumptions. Now the soft spots. The 31% listwise deletion is the load-bearing concern and the stress-test note is right to flag it. The authors acknowledge it, but the problem is real: if students with fewer recognition experiences skipped those items (plausible — you skip what you don't have), the analytic sample over-represents recognized students and inflates the associations. The null on misrecognition is equally vulnerable — differential missingness could produce a false null. Until multiple imputation is done, every coefficient and p-value here is provisional. The PCA is a secondary concern. Two components explaining 43% of variance is low, suggesting the recognition and misrecognition items are heterogeneous. The authors then shift to using individual items in the regressions rather than the components, which is actually defensible given the low variance explained — but the paper is unclear about when it's using components versus individual items, and that muddies the analysis. The retrospective self-report concern (college students recalling K-12 experiences) is real but standard for this kind of survey work. It's a limitation worth noting, not a disqualifier. The R² of 0.13 is low but not unusual for this kind of social science regression with attitudinal outcomes. The reader's scores are about right. Significance is modest — this confirms and extends existing frameworks rather than breaking new theoretical ground. The circularity concern is minimal; using a previously validated measure from a co-author's prior work is standard. Who is this for? STEM education researchers working on identity formation in minoritized populations, particularly people designing recognition-based interventions. It's a useful preliminary data point. My recommendation: this is a conference paper that the authors themselves call preliminary. It does not deserve a full journal-style peer review in its current form. The multiple imputation needs to happen first. If the results hold under MI, then it's worth a serious referee. Right now, it's an honest but incomplete analysis.

Referee Report

3 major / 8 minor

Summary. This manuscript examines associations between self-reported recognition and misrecognition experiences in STEM contexts and two outcomes—STEM identity and STEM career aspirations—among undergraduate students at Minority Serving Institutions (N=1,134). Using stepwise multiple linear regression (for STEM identity) and logistic regression (for career aspirations), the authors find that specific recognition experiences (being called upon, teacher recommendation, receiving awards) significantly predict both outcomes, while misrecognition experiences do not. The study draws on discipline-based identity theory (Hazari et al., 2010) and uses a previously validated single-item STEM identity measure (Dou & Cian, 2022). The authors acknowledge that findings are preliminary due to high missingness (31% listwise deletion) and state their intention to apply multiple imputation in future work.

Significance. The study addresses a practically important question for STEM education research: which specific recognition experiences are most strongly associated with STEM identity and career aspirations among minoritized students. The use of a large, multi-MSI sample is a strength, and the focus on event-based recognition (rather than general perceptions) is a useful operationalization. The single-item STEM identity measure is drawn from published, validated work by one of the co-authors, which is standard practice. However, the manuscript is currently formatted as a conference presentation summary rather than a full journal article, and the analytic foundation has two load-bearing issues that must be addressed before the central claims can be considered well-supported.

major comments (3)

§Findings, p. 8: The listwise deletion of 354 of 1,134 observations (31%) is the most serious concern. The authors acknowledge this and label findings as 'preliminary,' but the manuscript nonetheless draws substantive interpretive conclusions (e.g., that misrecognition is not associated with outcomes, that specific recognition experiences predict STEM identity). If missingness is related to either the predictors or outcomes (i.e., not MCAR), the reported coefficients and null results for misrecognition could be materially biased. The authors state they intend to use multiple imputation; this should be completed before the manuscript is submitted for journal review, as the current results are not publishable as stated. At minimum, a sensitivity analysis or missingness pattern diagnostic should be reported to assess the plausibility of MCAR.
§Analysis Procedures, p. 7: The PCA produces two components explaining only 43% of total variance (27% + 16%), yet the manuscript uses individual items (not component scores) in subsequent regressions. The relationship between the PCA and the regression models is unclear—if the PCA was conducted to address multicollinearity, the manuscript should explain how the PCA results informed the regression model specification. Currently, the PCA appears to be presented as justification but the regressions use the original items, leaving the analytical logic incomplete. The low variance explained also raises the question of whether the recognition items form a coherent construct, which should be discussed.
§Findings, p. 7: The logistic regression reports β = 0.47 (p = .002) for 'at least one recognition experience' predicting STEM career aspirations, with 1.59 higher odds. However, the manuscript also reports that individual recognition items (being invited to competitions, being accepted into STEM programs) were not significant. The construction of the 'at least one' variable and its relationship to the item-level results needs clarification—was this a composite variable, and if so, how was it created? The discrepancy between the composite and item-level findings should be explicitly discussed, as it bears on the interpretation of which specific experiences matter.

minor comments (8)

The manuscript is formatted as a conference presentation summary (NARST 2025) rather than a full journal article. The level of methodological detail (e.g., stepwise regression entry/removal criteria, PCA rotation method, assumption checks) is insufficient for journal publication. The manuscript should be expanded to include full methodological transparency.
§Data sources, p. 4: The demographic breakdown notes that respondents could identify with multiple racial/ethnic categories, but the percentages (48% Black, 34% Hispanic, 31% white) sum to >100% without explicit clarification that this is expected due to multiracial identification. This should be noted at the point of presentation.
§Survey Items, p. 5: The recognition and misrecognition items are described but the response format (binary? Likert?) is not clearly specified for all items. The STEM identity item is described as 5-point Likert, but the recognition/misrecognition items appear to be binary ('whether they were...'). Clarifying the measurement level of each predictor would improve clarity.
§Analysis Procedures, p. 7: The stepwise regression approach is mentioned but the entry/removal criteria (e.g., p-to-enter, p-to-remove) are not specified. Given that stepwise methods are sensitive to these thresholds, they should be reported.
§Findings, p. 8: The linear regression reports R² = 0.1342, which is modest. The manuscript should discuss this in terms of practical significance and the large proportion of unexplained variance.
§Findings, p. 8: The statement that demographic variables 'were not found to be significantly associated with STEM career aspirations' is reported without the corresponding statistics. These should be reported in a table or in-text.
The retrospective self-report design—college students recalling K-12 experiences—is a limitation that should be explicitly acknowledged. While the claims are associational (not causal), the possibility of recall bias (e.g., students with high STEM identity over-reporting past recognition) should be discussed as a threat to validity.
Figure 1 (PCA biplot) is referenced but not included in the manuscript text provided. This should be included with clear labeling of component loadings in the final version.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for a careful and constructive review. The referee raises three major concerns: (1) the 31% listwise deletion and associated risk of bias, (2) the unclear relationship between the PCA and the regression models, and (3) the construction and interpretation of the 'at least one recognition experience' composite variable. We agree with all three points and will revise the manuscript accordingly. Specifically, we will complete multiple imputation and report missingness diagnostics, clarify the role of the PCA, and explicitly describe the composite variable construction and discuss the discrepancy with item-level results. We note that the current manuscript is formatted as a NARST conference presentation summary, not a full journal article; the revisions described here will be implemented in the expanded journal-length version.

read point-by-point responses

Referee: The listwise deletion of 354 of 1,134 observations (31%) is the most serious concern. If missingness is not MCAR, the reported coefficients and null results could be materially biased. Multiple imputation should be completed before journal submission; at minimum, a sensitivity analysis or missingness pattern diagnostic should be reported.

Authors: We agree completely. The referee is correct that 31% listwise deletion is substantial and that the current results are not publishable as stated without further analysis. We labeled findings as 'preliminary' in the conference summary precisely because we recognized this limitation, but we concur that drawing substantive interpretive conclusions (including the null results for misrecognition) is premature given the missingness concern. In the revised manuscript, we will: (1) conduct and report Little's MCAR test or an equivalent missingness pattern diagnostic, (2) implement multiple imputation using chained equations (mice) with an appropriate number of imputed datasets, (3) re-estimate all regression models on the imputed data, and (4) report sensitivity analyses comparing results under listwise deletion versus multiple imputation. If the null results for misrecognition do not replicate under imputation, we will revise our conclusions accordingly. We will also temper all interpretive language until the imputation results are in hand. revision: yes
Referee: The PCA produces two components explaining only 43% of total variance, yet the manuscript uses individual items (not component scores) in subsequent regressions. The relationship between the PCA and the regression models is unclear. The low variance explained also raises the question of whether the recognition items form a coherent construct.

Authors: The referee is correct that the analytical logic is currently incomplete. The PCA was initially conducted as a diagnostic for multicollinearity among the recognition items, not as a basis for constructing component scores for the regressions. However, the manuscript does not make this logic explicit, and the reader is left uncertain about why the PCA is presented and how it informed the regression model specification. In the revision, we will: (1) clarify that the PCA served as a multicollinearity diagnostic rather than a variable-reduction step, (2) report the VIF statistics or condition indices that directly informed the multicollinearity assessment, and (3) discuss the low variance explained (43%) and what it implies about whether the recognition items form a coherent construct. If the PCA does not serve a clear purpose in the analytic pipeline, we will either remove it or restructure the analysis to use component scores consistently. We will also address whether the recognition and misrecognition items should be treated as separate constructs given the component structure. revision: yes
Referee: The logistic regression reports β = 0.47 for 'at least one recognition experience' predicting STEM career aspirations, but individual recognition items (being invited to competitions, being accepted into STEM programs) were not significant. The construction of the 'at least one' variable and its relationship to the item-level results needs clarification, and the discrepancy should be explicitly discussed.

Authors: The referee raises a valid point. The 'at least one recognition experience' variable was created as a binary composite indicating whether a respondent reported any of the five recognition experiences (called upon, teacher recommendation, invited to competitions, accepted into STEM programs, received awards). The manuscript does not clearly describe this construction, nor does it explain why the composite is significant when two of the five constituent items are not. This discrepancy is important for interpretation: it could indicate that the composite captures a general 'recognition exposure' effect driven by the three significant items, or that the combination of experiences matters beyond any single item. In the revision, we will: (1) explicitly describe how the composite variable was constructed, (2) present both item-level and composite-level results in a single table for direct comparison, and (3) discuss the discrepancy, including whether the composite result is driven primarily by the three significant items and what this implies for the practical interpretation of which specific experiences matter. We will also consider whether the composite approach is appropriate given the item-level heterogeneity, or whether reporting only item-level results would be more informative. revision: yes

Circularity Check

0 steps flagged

No significant circularity found; one minor self-citation for a validated survey measure that is not load-bearing for the central claims.

full rationale

This is an empirical education research paper using standard statistical methods (PCA, multiple linear regression, logistic regression) on survey data. The central claims are empirical associations between self-reported recognition experiences and STEM identity/career aspirations, tested via regression. There is no derivation chain that could reduce to its inputs by construction. The one self-citation present is Dou & Cian (2022), referenced for the single-item STEM identity measure ('I see myself as a STEM person'). While Dou is a co-author on both papers, this is standard practice in survey research—reusing a previously validated instrument. The measure itself is not the central claim being tested; it is an input variable. The regression analyses are standard statistical tests whose outputs (β coefficients, p-values, R²) are not determined by construction from the fitted parameters in a circular way. The paper acknowledges limitations (31% listwise deletion, preliminary findings pending multiple imputation) transparently. No step in the analysis chain exhibits self-definitional circularity, fitted-input-as-prediction, or ansatz-smuggling.

Axiom & Free-Parameter Ledger

2 free parameters · 3 axioms · 0 invented entities

The paper introduces no new theoretical entities, particles, or constructs. It uses existing frameworks (Hazari et al., 2010) and standard statistical methods. The free parameters are methodological choices (PCA thresholds, stepwise criteria) rather than theoretical parameters.

free parameters (2)

PCA component thresholds = Not specified
The paper does not specify the rotation method or extraction criteria for the PCA, implying these were chosen by the analysts.
Regression model entry/removal thresholds = Not specified
Stepwise regression is mentioned but the specific entry/removal criteria (e.g., p-to-enter, p-to-remove) are not stated, which are analyst choices.

axioms (3)

domain assumption Retrospective self-report of recognition experiences is a valid measure of actual recognition received.
The entire analysis depends on students accurately recalling and reporting whether they were 'often called upon,' 'recommended,' etc. This is invoked implicitly throughout the Data sources and Survey Items sections.
domain assumption The single-item measure 'I see myself as a STEM person' validly captures the STEM identity construct.
The paper cites Dou & Cian (2022) for validation, but the adequacy of a single-item measure for a complex construct like identity is a background assumption that the paper does not re-examine.
ad hoc to paper Convenience sampling from MSI English classes yields a sample representative enough for the regression analyses to be meaningful.
The sampling strategy (English instructors distributing surveys) is a practical choice that may introduce selection bias, but the paper proceeds with inferential statistics assuming the sample is adequate.

pith-pipeline@v1.1.0-glm · 10350 in / 2316 out tokens · 290966 ms · 2026-07-08T18:12:09.263035+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

3 extracted references · 3 canonical work pages

[1]

Avraamidou, L. (2022). Identities in/out of physics and the politics of recognition. Journal of Research in Science Teaching, 59(1), 58–94. https://doi.org/10.1002/tea.21721 Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191–215. https://doi.org/10.1037/0033-295X.84.2.191 Carlone, H. B., & Jo...

work page doi:10.1002/tea.21721 2022
[2]

J., Bush, S

Roberts, T., Jackson, C., Mohr-Schroeder, M. J., Bush, S. B., Maiorca, C., Cavalcanti, M., Schroeder, D. C., Delaney, A., Putnam, L., & Cremeans, C. (2018). Students’ perceptions of STEM learning after participating in a summer informal learning experience. International Journal of STEM Education, 5(1). https://doi.org/10.1186/s40594-018- 0133-4 Rodriguez...

work page doi:10.1186/s40594-018- 2018
[3]

https://doi.org/10.1371/journal.pone.0284945 Schmidt, J., Geith, C., Håklev, S., & Thierstein, J

PloS one, 18(4), e0284945. https://doi.org/10.1371/journal.pone.0284945 Schommer-Aikins, M., Duell, O. K., & Hutter, R. (2005). Epistemological beliefs, mathematical problem-solving beliefs, and academic performance of middle school students. The Elementary School Journal, 105(3), 289-304. Simpson, A., & Maltese, A. (2016). “Failure is a major component o...

work page doi:10.1371/journal.pone.0284945 2005

[1] [1]

Avraamidou, L. (2022). Identities in/out of physics and the politics of recognition. Journal of Research in Science Teaching, 59(1), 58–94. https://doi.org/10.1002/tea.21721 Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191–215. https://doi.org/10.1037/0033-295X.84.2.191 Carlone, H. B., & Jo...

work page doi:10.1002/tea.21721 2022

[2] [2]

J., Bush, S

Roberts, T., Jackson, C., Mohr-Schroeder, M. J., Bush, S. B., Maiorca, C., Cavalcanti, M., Schroeder, D. C., Delaney, A., Putnam, L., & Cremeans, C. (2018). Students’ perceptions of STEM learning after participating in a summer informal learning experience. International Journal of STEM Education, 5(1). https://doi.org/10.1186/s40594-018- 0133-4 Rodriguez...

work page doi:10.1186/s40594-018- 2018

[3] [3]

https://doi.org/10.1371/journal.pone.0284945 Schmidt, J., Geith, C., Håklev, S., & Thierstein, J

PloS one, 18(4), e0284945. https://doi.org/10.1371/journal.pone.0284945 Schommer-Aikins, M., Duell, O. K., & Hutter, R. (2005). Epistemological beliefs, mathematical problem-solving beliefs, and academic performance of middle school students. The Elementary School Journal, 105(3), 289-304. Simpson, A., & Maltese, A. (2016). “Failure is a major component o...

work page doi:10.1371/journal.pone.0284945 2005