Bibliographic record
Abstract
Action for Microfinance in Africa (AMAF), 24 African Private Equity and Venture Capital Association (AVCA), 25 Alternative financing method, 83 Arboribus, 140À145 Australia, 87 Australian Small Scale Offerings Board (ASSOB), 230 Bank of Central African States (BEAC), 22 Banking Commission of Central Africa (COBAC), 22 Behavioural Finance approach, 135 Benefit corporations, 137 Bombay Stock Exchange (BSE), 174 Broker-dealer license, 85 Canada, 86 Canadian Securities Administrators (CSA), 86 Capital assembly in crowdfunding platforms, 103À104 narrative and entrepreneurial, 104À106 Capital Asset Pricing Model (CAPM), 132 Cash management, 131, 138 Cash surpluses, 131, 136, 147 Catarse, 152, 153, 163 CEMAC.See Economic and Monetary Community of Central Africa (CEMAC) CF.See Crowdfunding (CF) CFP.See Crowdfunding platforms (CFP) COBAC.See Banking Commission of Central Africa (COBAC) Combat fraud, 13À14 Community benefits, 5 Community funding, 24 Corporate finance, 130 Corporations and Market Advisory Committee (CAMAC), 87 Credit Guarantee Fund Scheme, 174 Cross-side network effects, 6À7 Crowd, 203, 204À211 democratic power of, 207À208 with generosity, 210À211 impulsiveness of, 205À207 indifference of, 211 online, 212À215 suggestibility of, 209À210 wisdom of, 204À205 Crowdfunding (CF) academic authors, xxviiÀxxix academic institutions, xxixÀxxxi academic literature on, xxviÀxxvii in Australia, 87 backers and campaign duration, 112, 121 in Canada, 86 complexity, xxii computer-mediated phenomenon, 38 context, 108
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.671 | 0.676 |
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".