Bibliographic record
Abstract
Méthodes de recherche en management. Thiétart, Raymond‐Alain et coll. Doing Management Research—A Comprehensive Guide. Thiétart, Raymond‐Alain et al Abstract This book is a fine achievement. Recently translated from the French and specially adapted to Anglophone audiences, it is now available in North America under the aegis of Sage Publications. The objective of the book—to provide a comprehensive yet simple guide for “doing management research”—is fully attained. The result of a three‐year collaboration among 22 European researchers, the book is built around the major issues that researchers preparing for a significant project must confront upon putting together a research proposal. One of its merits is its original combination of French clarity and North American pragmatism. The mix of soul‐searching issues, principles, techniques, and applications sets it apart from the current production in the field. Readers are always made aware of the choices they can make and the consequences of each choice for the future of their research project. Another merit of the book is its full integration across chapters, both in contents and style. The approach is unusually balanced, as quantitative and qualitative research methods are given equal status. The main pedagogical messages the book conveys are that research questions should drive methodology and not the other way around, that methodological purism is often unproductive, and that researchers must continually strive to maintain consistency between their objectives and their methods. A vast array of real research examples taken from empirical articles or monographs helps make sense of the issues discussed. For its exceptional integration between sound principles and practical methods, the book is definitely a must on the shelves of graduate students and professors of management alike.
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.174 | 0.128 |
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".