Bibliographic record
Abstract
C J H P – Vol. 61, No. 4 – July–August 2008 J C P H – Vol. 61, n 4 – juillet–aout 2008 volunteering isn’t just a fact of life. It feels good, leaving you with a sense of pride and accomplishment. And all volunteer activities add to the breadth and depth of knowledge and experience that you bring to the other aspects of your life. As I prepare to take over as chair of the CSHP Finance Committee, I would like to thank Moira Wilson for her outstanding leadership as my predecessor in the director of Finance position. I would also like to thank Cheryl McGrath-Hill and Cathy Lyder, 2 members of the Finance Committee who have recently resigned, for their invaluable contribution to CSHP. Thornton Wilder said that “money is like manure— it’s not worth a thing unless it’s spread around encouraging young things to grow”. I’ve heard many times over the past few years that there are plenty of financial resources within CSHP that could be put to good use for member services. The only thing missing is human resources—people—to plan and organize how to put that money to use for members. Don’t let the potential of CSHP fail to flourish through neglect. I challenge everyone who has never served on a CSHP committee to join one, either at the branch or at the national level. We have the “manure”; now let’s spread it around!
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.057 | 0.006 |
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".