Bibliographic record
Abstract
It takes a village…This phrase comes to mind when I reflect on the past year. In 2007, what COSTI defines as “community development” represented the organization’s focus and greatest area of growth. Services that cater to the individual alone, and to his or her transition to Canadian life are not enough. Equally important is the need to build welcoming communities. To build successful, thriving communities, systemic barriers must be broken down and a network of community supports created; leadership and capacity building must also be developed. These are critical aspects to achieving a harmonious, inclusive and equitable multicultural society. COSTI makes a significant investment in community development and takes a broad-based approach that is intrinsic in all service areas. COSTI staff contributed over 12,000 hours and participated in over 40 sectorspecific, ethno-specific and issue-specific work groups and coalitions – whether to develop or coordinate services among providers, support a local community initiative, conduct research, or raise awareness and recommend solutions to policymakers. COSTI also supports sector capacity building through projects that extend resources and support to various community agencies through resource development, training and partnerships.
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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.192 | 0.176 |
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".