Bibliographic record
Abstract
In this issue of JISIB we bring you articles from two different conferences held this spring. The first was the 7th International Competitive Intelligence (ICI) conference held in Strasburg 25-26th March. The second was the IT Management Annual (AIM) conference held in Rabat May 20-22. The journal would like to thank the organizers of these conferences for a fruitful cooperation, where JISIB editors have served as reviewers of scientific track papers and best paper awards.To keep up with the journals new aim to publish case studies Calof et al. present the story of Nortel, a Canadian telecommunications and data networking equipment manufacturer which went bankrupt in 2009.As always we would first of all like to thank the authors for their contributions to this issue of JISIB.On behalf of the Editorial Board,
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.053 | 0.051 |
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".