Bibliographic record
Abstract
So much sweat and effort -our hard work is almost done To craft a paper second to none Months of polishing our research design And perfecting every line.But every moment was worth it, we are ready for the peer-review process to begin, So excited to send it in!One final step, to prepare the letter To send along to the editor In which we can highlight the paper's ingenuity And its contribution to the management education community.The last editorial, we discussed the conference presentation to publication pipeline.This was the first in a series we hope to write over the next year, to support more authors having a constructive and productive experience in the peer-review process.Notably, we sometimes find that authors cannot gain access to peer-review and are stopped at the very beginning, due to a lack of preparation or an incomplete understanding of the whole process.This is a fact that former JME editor Jon Billsberry laments in his 2014 editorial where he shared that "[o]ne of my greatest sorrows as editor is that so many manuscripts we receive are rejected at the first hurdle" (p.3).In this editorial, we
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.
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 | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.568 | 0.454 |
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".