Bibliographic record
Abstract
Lena K. Soots is a PhD candidate in Geography at Simon Fraser University and works as a researcher and instructor at the Centre for Sustainable Community Development at SFU. Lena has been working with BALTA since 2006 on various research projects. Lena's academic background includes a Masters in Environmental Studies from the University of Waterloo. Her professional background includes work in environmental consulting, community economic development, and community consultation and facilitation. About the British Columbia – Alberta Social Economy Research Alliance (BALTA) BALTA is a regional collaboration amongst universities and social economy stakeholder organizations engaged in research initiatives to strengthen the foundation of the social economy in BC and Alberta. BALTA is a five year research project (2006-2011) funded by the Social Sciences and Humanities Research Council of Canada (SSHRC). The overall project is working towards the reinsertion of social goals, reciprocity and solidarity into economic thinking and decision making, and aims to address the following primary research questions: 1. What are the scope and characteristics of the social economy in BC and Alberta? 2. What are the scope and characteristics of social economy innovations that are achieving demonstrable social and economic results, in the region and elsewhere? 3. What are the key issues, opportunities and constraints for adapting and scaling up whatever is working, both within and outside the region?
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.710 | 0.526 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".