Bibliographic record
Abstract
The ACCOLEDS 2007 Workshop provided the opportunity to try several methods of retrieving, manipulating and displaying data from Statistics Canada. \n \nWe received an update on services available through the Data Liberation Initiative (DLI). We used ArcGIS, GeoSuite, and MapPoint for mapping data. As I don’t have a geography background this was very informative as to understanding more fully the information needs of those in working in geography. \n \nInternational trade data was one topic area discussed that will be useful in serving AU Library patrons in business, global studies, and other fields. \n \nWe had several hands-on workshops including using excel to manipulate public use microdata files. This certainly improved my excel computer skills. \n \nOne presentation was from a public administration professor at the University of Victoria, discussing how she integrates economic and geographic data into her classroom. This was useful in furthering my understanding of various approaches to data and how working with data can achieve particular learning outcomes. \n \nWorking with data is difficult and this provided me with an opportunity to increase my comfort with serving AU Library patrons’ data questions.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.837 | 0.595 |
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".