Bibliographic record
Abstract
The stimulus for this special issue arose from a conference held by Laboratory Interdisciplinary Gestion University Enterprises (LIGUE) in Tunisia in 2018.LIGUE is an interdisciplinary research laboratory and is the first in Management Sciences to be created in Tunisia in 1999 at the High Institute of Accountancy and Business Administration of the University of Manouba.The conference was organised in collaboration with the Academy of Marketing B2B SIG, Bournemouth University, UK, and the R&DM Research Center FSA -University Laval, Quebec.Such was the interest, commitment and contribution from the participants that the conference organisers made plans to build on the research being undertaken in north Africa and extend it to include the Middle East, i.e.Middle East and North African (MENA).The MENA region is made up of 21 countries as follows: Algeria, Bahrain, Djibouti, Egypt, Iran, Iraq, Israel, Jordan, Kuwait, Lebanon, Libya, Malta, Morocco, Oman, Qatar, Saudi Arabia, Syria, Tunisia, United Arab Emirates, Palestine and Yemen.The area has represented an under explored context in marketing research (Okazaki and Mueller, 2007;Al-Olayan and Karande, 2000) for a number of reasons: government barriers and the lack of priority given to research (Lages et
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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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.066 | 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".