Bibliographic record
Abstract
What does a multinational approach to team-building look like? This case explores why, on the surface, one team was succeeding brilliantly while another team was languishing. Mounting problems at one of Fiduciary Consulting's fledgling business units in Boston required the immediate attention of the company's Montreal-based CEO, Jim Smith. Fiduciary had hired a new CEO for the U.S. subsidiary in 2003, and now, only nine months later, there seemed to be a lot of turmoil in that office. He began to wonder whether the subtle grumblings of his colleagues in Boston should have warranted a response from him earlier. All he could do at this stage was to think about what his expectations for success should be in the United States. What was going on in that office? How should he try to sort out the issues that seemed to be percolating? With all the success and collegiality that surrounded him in Montreal, Smith wondered what had gone so wrong in the Boston office––and what he could do to rectify the situation.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.047 | 0.012 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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".