Informing Minority Communities (Abstract only)
Bibliographic record
Abstract
October 2001, The McGill Consortium for Ethnicity and Strategic Social Planning issued a report on the Black Community of Montreal, “Evolution of the Black Community of Montreal: challenge and change.” While the Report is very informative about the position of Blacks in Quebec and Canada, it does not specify the indicators of change, the measurement and rate of change. However, the Report’s primarily cross sectional data paints a convincing picture of the challenges Blacks in Quebec face, and their disturbing under-representation in the economic, political, and decision making spheres of the society. A paper present at ISRI Conference, Barcelona, July 2008, shows that over the years opportunities presented for the survival and wellbeing of these groups were minimal and bleak. Yet they have managed to survive and shown some progress and growth over the last forty years. In the face of these challenges, what has sustained these minority communities and more importantly, in what areas, if any, have they moved to higher levels on the fitness landscape index? Both Bayne and Torczyner have indicated that there has been growth in some areas, but are deeply concerned about the sustainability of any observed development that has taken place. In our endeavour to understand minority immigrant entrepreneurship, we pose the following question: do certain immigrant or minority groups have the kinds of structures and capacity for producing sufficient ingenuity that will enable them to create search procedures that will move them to higher levels of existence and/or better life styles?
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.104 | 0.011 |
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".