Bibliographic record
Abstract
Abstract Add Introductory Brochure To Publication Offerings You Can Make It Happen Writing That Gets Results Messages Challenge Donors to Upgrade Giving Levels Reward Great Ideas Mailing List Tips Overcoming Call Reluctance Broadening Your Donor Base Target Large Groups With Special Offers Funding Opportunities Technology Grants Available for 2002 If You're Considering a Job Change Get a Reading On Your Prospective Employer's CEO Trends In Philanthropy Canadian Giving On the Rise Telesolicitation Stewardship Form Invites Donors to Evaluate Phonathon Callers Donor Retention Techniques Honor Roll ‘Draft’ Gets Lapsed Donors Back Combine Your Request and Response Form Are You Making Adequate Use Of Your Advisory Boards? Websites Should Include Mission Statements Tips For Avoiding Fund‐raising Burnout Fund‐raising Ethics The Ten Rules of ePhilanthropy Useful Reports Help Your Staff Monitor Year‐to‐date Giving Totals On the Lighter Side How To Acknowledge Gifts of Stock How to Remember Names Issues for Member‐driven Organizations Membership Versus The Annual Fund: Explaining Members the Importance of Both Researching Foundations Good Research Determines Best Way To Make Initial Approach Press Conferences That Impact Giving Keep the Media's Needs in Mind During Press Conferences Donor Recognition Ideas Engraved Bowling Balls Honor Donors Envelope Acronyms Effective Retention Techniques First‐time Donors Receive a Special Letter, Invitation to Become Involved
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.893 | 0.842 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".