Social Innovation Partnerships: An Opportunity for Critical, Activist Scholarship
Bibliographic record
Abstract
From 2013–2015, I was a Mitacs Elevate postdoctoral fellow with Mozilla. The program of research aimed to foster digital literacy capacities among youth and informal educators in the Greater Toronto Area (GTA) and specifically examined the extension of the Mozilla community’s free and open source software production practices to build a digital literacy network called Hive Toronto. This article presents results from a document analysis (n = 21) of applications, blogs, and other materials revealing how the people, challenge, practices, and results associated with social innovation unfolded through research with Hive Toronto. Based on these findings, the article demonstrates that tensions linger between industrial and social innovation funding, but that there is space for critical and activist research when building such partnerships.
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 | 0.036 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.054 | 0.059 |
| Scholarly communication | 0.039 | 0.025 |
| Open science | 0.005 | 0.043 |
| Research integrity | 0.013 | 0.016 |
| Insufficient payload (model declined to judge) | 0.013 | 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".