Going Public: Debating Matters of Concern As an Imperative for Management Scholars
Bibliographic record
Abstract
According to a recent Bloomberg Businessweek cover story, the United States is poised to become the largest oil producer in the world by the end of 2018 (Blas, 2018). This is a remarkable development, considering that the U.S. imported more than 12 million barrels per day as recently as a decade ago. In fact, the United States is now producing so much oil that it repealed a 40-year ban on oil exports and, by 2017, was shipping some 1.3 million barrels per day to buyers in China, India, South Korea, Venezuela, and elsewhere (Krauss, 2017). At the heart of the United States’ transformation into an energy superpower is an extraction process colloquially known as “fracking.”[...]
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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 | 0.125 | 0.276 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.031 | 0.127 |
| Scholarly communication | 0.033 | 0.043 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.042 | 0.038 |
| Insufficient payload (model declined to judge) | 0.004 | 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".