Evaluation Of The Oasis Computerization Project: Final Report
Bibliographic record
Abstract
In 1997, Citizenship and Immigration Canada’s Ontario Administration of Settlement and Integration Services (OASIS) initiated a $12.5 million Computerization Project to build the capacity of agencies delivering its ISAP (Immigrant Settlement and Adaptation Services), HOST, and LINC (Language Instruction for Newcomers to Canada) programs. The Computerization Project ended March 31, 2002. Prior to moving ahead with new computerization investments for the agencies, CIC hired RealWorld Systems, a technology consulting firm, to evaluate options for future support. The three key research questions addressed in the course of this evaluation were: - How does the computerization project, with all its elements (LINC computerization, ISAP/HOST computerization, Settlement.org Internet site and Extranet), impact services to immigrants and refugees? - How should CIC invest in computerization over the next three years? - What changes, supports, and improvements are needed to the computerization project to further benefit immigrants, refugees and the agencies and staff who work to support them?
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 | 0.088 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".