Bibliographic record
Abstract
ACMLA NEWSIASSIST and the ACMLA would like to invite you to join us for the 49th Annual IASSIST conference and 57th annual Carto conference being jointly held in Halifax, Nova Scotia, from May 28-31, 2024 to talk about the future of data in libraries, archives, and data services.The motto of Halifax is "e mari merces", or "wealth from the sea"; this wealth was originally measured in fish, but today we could equally think of the wealth to be found in an ocean of data.Artificial intelligence, climate change, and a host of other influences are moving us into uncharted territory.This conference challenges you to chart new pathways and to think about using data to navigate our way to a better future together.The conference will be held in-person, centering networking opportunities and interaction.We welcome submissions for papers, presentations, posters, demos, workshops, and lightning talks that embrace our conference theme, "Uncharted: Navigating the future of data," by looking towards emerging trends and topics of particular relevance to data and geospatial professionals
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.830 | 0.755 |
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".