Toddling Towards Childhood: A Bibliometric Analysis of the First QROM Lustrum (2006 - 2010)
Bibliographic record
Abstract
The first years in the life of a journal are the most difficult ones as editors need to advertise it effectively and attract worldwide researchers to safeguard its launch and maintenance. This study provides a bibliometric analysis of the first lustrum of the journal Qualitative Research in Organizational and Management (QROM) in an attempt to assess its production both in methodological and conceptual terms. The sample was made up of 66 articles by 109 (co - )authors from 66 institutions. A total of 53.2% of contributors were female and were responsible for 42.4% of the single - authored articles (compare to 34.8% of only - male articles). Eight “invisible schools , ”, 37.5% national ones, were obtained by relating authors to sharing co - authors (grade 1), institutions (grade 2) or cities (grade 3). The most productive authors were Cassell, Grandy, and McKenna, the first two being developers of invisible schools. The number of articles, theoretical perspectives, and diversity of applied techniques has increased in QRO M over the lustrum period with UK and Canada as most prolific countries followed by USA, Sweden, and Australia. Most articles dealt with organizational and managerial issues under discourses or narrative perspectives using interviews and sense - making theories. The evolution of these findings is also presented.
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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 | 0.009 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.115 | 0.122 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".