Let me in: Building an I-O bridge that combats the subtle redlining of the scientist–practitioner gap
Bibliographic record
Abstract
Amid discussions of the scientist–practitioner gap in I-O, it is often assumed that there is a single bridge to cross that has two parties on either side: academic researchers and practitioners. However, whether intentionally or not, small businesses have largely been neglected in efforts to fortify the bridge between the aforementioned parties, leaving the route between academic research and small businesses poorly maintained. As such, our commentary will focus on various aspects of the points of invitation for debate provided in Zhou et al. (Reference Zhou, Campbell and Fyffe2024), namely the prevalence of the scientist–practitioner gap between I-O researchers and small business owners and the access small businesses have to academic research insights. Although all authors on this paper are I-O-trained researchers working in and contributing to the academic field, two coauthors are also small business owners, adding nuanced richness to this conversation. Further, as diversity, equity, and inclusion (DEI) researchers, we consider equitable access to the knowledge generated by academic research as a key principle to our work.
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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 | 0.022 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.014 |
| Scholarly communication | 0.020 | 0.030 |
| Open science | 0.004 | 0.046 |
| Research integrity | 0.014 | 0.018 |
| Insufficient payload (model declined to judge) | 0.067 | 0.036 |
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".