A visual tool for structuring and modeling organizational memories
Bibliographic record
Abstract
Article A visual tool for structuring and modeling organizational memories Share on Authors: Tang-Ho Lê University of Moncton, Computer Science Department, Moncton (NB) Canada E1A 3E9 University of Moncton, Computer Science Department, Moncton (NB) Canada E1A 3E9View Profile , Luc Lamontagne Defence Research Establ. Valcartier 2459 Blvd Pie-XI north, Val-Bélair (Qc), Canada G3J 1X5 Defence Research Establ. Valcartier 2459 Blvd Pie-XI north, Val-Bélair (Qc), Canada G3J 1X5View Profile , Tho-Hau Nguyen Univ. du Québec à Montréal, CP. 8888, succ. Centre-Ville, Montreal (Qc), Canada H3C 3P8 Univ. du Québec à Montréal, CP. 8888, succ. Centre-Ville, Montreal (Qc), Canada H3C 3P8View Profile Authors Info & Claims CIKM '00: Proceedings of the ninth international conference on Information and knowledge managementNovember 2000 Pages 258–263https://doi.org/10.1145/354756.354827Online:06 November 2000Publication History 1citation541DownloadsMetricsTotal Citations1Total Downloads541Last 12 Months3Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 0.007 |
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".