Project LINQ
Bibliographic record
Abstract
We students in Professor Carol Bogash’s Fall 2025 Sourcing, Securing and Servicing Partnerships course at the University of Maryland Baltimore County were given the mission of identifying, evaluating, prioritizing and recommending potential partners for the Maryland Department of Aging (MDOA) to consider while implementing the Longevity Ready Maryland (LRM) initiative. Accordingly, we focused our efforts on meeting that challenge and as the semester comes to a close, we are now ready to share details of theprocess we utilized to flesh out prospects and rate their sutibaility, and also to submit our final partner recommendations to the MDOA. To open our work on this project, we divided into four groups and began by familiarizing ourselves with the LRM and the Epic Goals and their corresponding Objectives. We then moved to our research and brainstormingphase, identifying an initial group of potential partners. Next we developed a series of evaluation forms to rate our prospective partners in a standardized and objective manner, and subsequently utilized the resulting evaluations toprioritize our recommendations. Finally, we compiled our information into this document to offer a comprehensive and robust explanation of the process and provide our prioritized partner recommendations.
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 | 0.028 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.325 | 0.101 |
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".