Affective Identity Predicts Entrepreneurial Intent with Two Forms of Self-Entrepreneur Congruence
Bibliographic record
Abstract
Vocational psychologists have theorized that the congruence between self and occupations is the key to find fulfilling careers for individuals (Vondracek & Porfeli, 2011). However, the typical use of vocational interests to capture information about the self has been limited because it does not disentangle identity and work preferences in people’s responses in vocational assessments. People cannot be fully informed of careers most fitting to them if the vocational assessment does not capture distinct information about their identity. In this study, we strive to disentangle identity from preferences by including affective identity, which is sentiments that people hold towards themselves, as a predictor for career intent. Focusing on the context of entrepreneurship as a career, we examine how the congruence of affective identity and affective ratings of entrepreneurs provide additional information in predicting entrepreneurial intent beyond work preferences congruence. We invited undergraduate students from a Canadian University to complete an online-survey for an extra credit in their psychology course. We examined the impact of different congruence form of intent by including linear and polynomial terms of self and entrepreneur ratings when conducting a hierarchical linear regression. In general, we found support for the validity of our developed measure and demonstrated that contemporary congruence forms based on factors of affective identity brings new information in career choice perception. Affective identity accounts for unique predictability of self perception beyond vocational preference, which suggests the potential use of affective identity for career search feedback.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".