Human resources: finding (and keeping) the right staff
Bibliographic record
Abstract
“Teamwork” is a huge buzzword in modern business, with the ability to create and/or assemble a winning team considered to be one of the hallmarks of leadership. For a team to function well, there must be mutual trust, respect and cooperation. While each member of a strong, successful team has the knowledge, skills and confidence to be a “star” in their own right, they also understand that this talent is shared by all the members of the team – and they each have the generosity of spirit to allow everyone to shine. It is precisely because each person in a winning team is a “star” that they are sought after by competitors who are hoping to create their own winning team. It is then incumbent upon the manager of a winning team to ensure that the effort and success of everyone in the team is recognized and rewarded – otherwise the team might be lost. It is the same for the IVF Center, and for the IVF Lab, since a strong, functioning team is probably the greatest key to success. Recruitment and retention of good embryologists is a challenge. However, it is a challenge which must be met, because if you don't respect and look after your people, you have a fundamental flaw in your approach to Quality. This is also a fundamental failing for accreditation.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.045 | 0.037 |
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".