Strategy Research at Crossroad- Interesting Papers in Strategy: What Are They & How Do We Know?
Bibliographic record
Abstract
Strategy research has evolved significantly in the last decades. The evolution has resulted in a multidisciplinary field with a remarkable topic and methodology diversity. This trend, while adding to the richness of the field, has rapidly changed the landscape of publishing, posing challenges to scholars, journals and readership. This symposium focuses on these challenges by discussing what constitutes a good paper in strategy. A panel discussion will be conducted by a set of leading scholars from a wide range of backgrounds and cohorts addressing some deep and debatable issues regarding the future of strategy research.
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 | 0.131 | 0.432 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.021 | 0.026 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.080 | 0.067 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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".