A Re-Examination of Racioethnic Imbalance of IS Doctorates: Changing the Face of the IS Classroom
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it