Bibliographic record
Abstract
J C P H – Vol. 59, n 3 – juin 2006 164 healthy discussion within the Society and has led to the creation of a task force that will assess the ways in which CSHP can champion the leadership agenda. During its February 2006 meeting, Council reviewed a 12-month trial of an additional professional staff position in the CSHP office and, on the basis of the positive impact analysis, rendered the position permanent. The presence of a second pharmacist in the office has proven to be of significant value in increasing services to members and in facilitating timely responses to external consultations. The foresight of Council in creating this position clearly demonstrates the type of vision and leadership that will continue to advance the Society and improve services to you, the members. At the 2006 Professional Practice Conference earlier this year, CSHP initiated a strategic planning process to ensure the success of CJHP. This session has already led to improvements in business processes for the Journal. All of these achievements and more demonstrate the power of teamwork. Being part of Council and working with Council members has been both professionally and personally rewarding, and I am sure that I will look back on this experience with great fondness.
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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.007 | 0.016 |
| Insufficient payload (model declined to judge) | 0.117 | 0.058 |
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".