What Advice Current Pathology Chairs Seek From Former Chairs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The 2018 Association of Pathology Chairs annual meeting included a panel discussion of Association of Pathology Chairs senior fellows (former chairs of academic departments of pathology who have remained active in Association of Pathology Chairs) about the type of advice that current (sitting) pathology chairs ask them. To inform the panel discussion, information was obtained from the senior fellows by e-mail and subsequent conference call. Of the 33 respondents, 24 (73%) had provided consultation advice (9, <5; 11, 5-10; 2, 10-20; and 2, >20). Most (>75%) of the consultations were provided face-to-face and outside the framework of Association of Pathology Chairs, with 70% of those seeking advice being well known by the consultant(s). Of the senior fellows providing advice, 71% had themselves sought consultation from former pathology chairs and 75% from nonpathology chairs. Modest correlation was found between the number of consultations senior fellows sought when they were chairs and the number of consultations they subsequently provided. The most frequent topics of consultation were strategic planning, balancing the missions, setting department priorities, recruitment of faculty and staff, conflict management, issues specific to new chairs, and resource (money/space) issues. Those who had provided such advice the longest and to the most people indicated that there was no significant change in the type of questions asked over time. Former department chairs can be a valuable source of counseling for current chairs, and organizations of department chairs should consider formalizing the use of these individuals as consultants to sitting chairs.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it