Bibliographic record
Abstract
The theme of the next Academy of Management conference, to be held in 2020 in Vancouver, Canada, is "Broadening Our Sight" (Aguinis, 2019), which sounds quite fitting in today's times. Instead of regretting why business administration research does not always enjoy the same prestige as other areas [a subject well studied by Khurana (2007)], the call for papers for this conference expects researchers to abandon the zero-sum thinking present in the dichotomies that surround business administration research (e.g., dilemmas such as qualitative or quantitative research? Research on the micro, meso, or macro level?). The conference seeks contributions that go beyond this binary model, which is not quite useful for building synergies in the search for solutions. However, the issue is not just internal organizational problems. External problems such as political strategies, supply chain, and people management or forms of leadership—among many other topics addressed in business administration research—are definitely associated with management. Polarized positions do not contribute to creative solution of problems (but diversity and pluralism do), and the complexity of the contemporary scenario requires solutions that combine diverse areas of knowledge. The domain of business administration needs to reconcile the internal difficulties faced by companies with the political and social issues that surround them.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.056 | 0.023 |
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".