Bibliographic record
Abstract
In Canada, the non-profit organizations (NPO) and settlement sectors are increasingly re-examining their responsibility for service delivery and service design. With a growing interest in understanding how to include design principles and an “innovation” mindset in addressing the long-term outcomes of social services, new instruments are introduced as a way to experiment with different modes of engagement among the various stakeholders. The aim of community hackathons or civic hacks—a derivative of tech gatherings customized to fit public engagement—is to collaboratively rethink, redesign, and resolve a range of social and policy issues that communities are facing, from settlement, the environment, health, or legal services. Although hackathons and civic hacks aspire to be democratic, relationship-driven instruments, aligned with non-profit principles of inclusion and diversity, they are also risky propositions from the perspective of the non-profit organizational culture in Canada in that they tend to lack solid structure, clear rules, and fixed outcomes. Despite the challenges, the promise of innovation is too attractive to be disregarded, and some non-profits are embarking (with or without the government’s help) on incorporating hackathons into their toolkits. This case study will present a practitioner’s perspective on the outcomes of two community hackathons, one exploring migration data sets and the other on language policy innovation, co-developed between 2016 and 2019 by MCIS Language Solutions, a Toronto based not-for-profit social enterprise, in partnership with various partners. The case study examines how the hackathon as an instrument can aid settlement sectors and governments in fostering non-profit innovation to rethinking the trajectory of taking solutions to scale.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".