Relationship between managerial values and hiring preferences in the context of the six decades of affirmative action in India
Bibliographic record
Abstract
Purpose The purpose of this paper is to explore the relationship between managerial values and preference for hiring of low caste and female job candidates in the context of the six decades of affirmative action in India. Design/methodology/approach A sample of managers from India filled in a questionnaire indicating their beliefs and values concerning the Indian reservation system, social activism and minority employment. Subjects also made hiring choices in a simulated decision environment. Findings Findings indicate that managers were marginally in favour of hiring minority candidates and that their values and beliefs concerning minority employment of low caste and female job candidates were mixed. Research limitations/implications The study used self‐reported questionnaires, and the sample size was small. Future studies are recommended to overcome the limitations. Practical implications Managers responsible for making hiring decisions should be trained and educated in the need for equity, justice and diversity in the workplace. Originality/value This investigation provides empirical evidence linking managerial beliefs and values to hiring preferences of minority job candidates.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Survey of managerial values and minority hiring preferences; the workplace, not the research workforce.
The study examines managerial values and hiring preferences in India.
Managerial values and hiring preferences under Indian affirmative action is HR/management research, not metaresearch.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".