Analysis of papers from twenty-five years of SIGIR conferences
Bibliographic record
Abstract
article Share on Analysis of papers from twenty-five years of SIGIR conferences: what have we been doing for the last quarter of a century? Authors: Alan F. Smeaton Dublin City University, Glasnevin, Dublin, Ireland Dublin City University, Glasnevin, Dublin, IrelandView Profile , Gary Keogh Dublin City University, Glasnevin, Dublin, Ireland Dublin City University, Glasnevin, Dublin, IrelandView Profile , Cathal Gurrin Dublin City University, Glasnevin, Dublin, Ireland Dublin City University, Glasnevin, Dublin, IrelandView Profile , Kieran McDonald Dublin City University, Glasnevin, Dublin, Ireland Dublin City University, Glasnevin, Dublin, IrelandView Profile , Tom Sødring Dublin City University, Glasnevin, Dublin, Ireland Dublin City University, Glasnevin, Dublin, IrelandView Profile Authors Info & Claims ACM SIGIR ForumVolume 36Issue 2Fall 2002 pp 39–43https://doi.org/10.1145/792550.792556Published:01 September 2002Publication History 30citation337DownloadsMetricsTotal Citations30Total Downloads337Last 12 Months12Last 6 weeks1 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 | 0.026 | 0.173 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.156 | 0.156 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.019 |
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".