Social Capital and Aboriginal Communities: A critical assessment Synthesis and assessment of the body of knowledge on social capital with emphasis on Aboriginal communities
Bibliographic record
Abstract
The social capital paper consists of four sections. Synthesis and Assessment of the Body of Knowledge on Social Capital with Emphasis on Aboriginal Communities provides a general background and overview of the concept of social capital, discussing its intellectual history and examining the areas of study where the notion of social capital has been applied to. It reviews in-depth the use of the concept of social capital for the study of Aboriginal communities. The Measurement of Social Capital and its Impacts with Emphasis on Aboriginal Communities examines the literature related to the measurement of the concept of social capital and synthesizes research that has sought to assess the impact of social capital on various societal outcomes. Influencing Social Capital: A Review of the Literature with Emphasis on Aboriginal Communities reviews both research and policy initiatives that sought to impact social capital of communities and understand the potential mechanisms at play. It presents some case descriptions and addresses the evidence related to influencing social capital and potential interventions to that effect. Aboriginal Social Capital: A Review of the Literature pulls together the main conclusions from the previous documents and provides guidance in relation to the potential of social capital as a notion for research and practice among Aboriginal communities.
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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 | 0.057 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.010 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".