A Re-Examination of Racioethnic Imbalance of IS Doctorates: Changing the Face of the IS Classroom
Bibliographic record
Abstract
There is an extremely low percentage of minority faculty in the IS field. This global trend is highly conspicuous-- a minority of blacks compared to a majority of white academics in England, a minority of Aborigines compared to a majority of white academics in Australia, a minority of blacks compared to a majority of white academics in Canada, and for the purpose of our study, a minority of Native American, Hispanic American, and African American academics compared to a majority of white academics in the United States. Between 1995-2000, not only do AACSB reports indicate a continuous decline in minority business doctorates, but the accreditation body reports that the IS discipline shows a significant under-representation of minority faculty. In this study, we argue that mentoring under-represented groups in the discipline offers the field a myriad of avenues to change the ¡°face¡± of the classroom and reduce this gap. We examine the absence of racioethnicity and mentoring in the IS field and offer lessons learned from the Ph.D. Project Model for engendering change and mentoring within the IS community. Using data from a six-year period, we discuss diversity issues, lessons learned, and recommendations from mentoring a group of under-represented IS doctoral students.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 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".