Policies that support bridging, bonding and building between government and the social economy in Atlantic Canada: Policy Scan Process Report 2009. Annotated Bibliography
Bibliographic record
Abstract
This literature and web-resource list is produced as part of SEPROJECT‘s sub-node 1 Policy research reviewing policies that support bridging, bonding and building between government and the social economy in Atlantic Canada. As part of the process of creating an inventory of policies and programs supporting the social economy in the region and providing an analysis and definitional discussion and working papers on policy and social economy, a number of sources were researched to provide background literature review and resources. Some of these are reproduced here as a general resource and 'by-product' of the primary research focus. \n \nA range of sources (regional, national and international) accessible by the internet were used e.g. bibliographic, library and journal databases such as Ingenta, Emerald; government sources e.g. departmental websites and parliamentary party web pages; non-government and community based sources e.g. locally based or specialist social economy organisation sites and apex organisation sites; research institute sources e.g. independent and university based research centres; and more general and popular web search facilities such as Google and Google Scholar.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.019 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.001 |
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".