Civically Speaking Rebroadcast on Saskatoon's Community Radio Station CFCR 90.5 FM June 4, 2019 with Melanie Morrison and the Bettercart.ca
Bibliographic record
Abstract
In this rebroadcast of Civically Speaking, a spoken word show devoted to civic issues hosted by Lenore Swystun on CFCR.ca 90.5 FM, Melanie Morrison comes on to discuss an App she recently developed called the BetterCart.ca Melanie Morrison, by day, a Social Psychology Professor, spends much time during her evenings and weekends, developing an App to help folks find the best, most affordable deals on groceries. Melanie shares her story about how she went from being a single mom of three kids going to University trying to make ends meet, to developing an app to help make such ends meet.Melanie recently won a tech start-up competition, where she pitched her idea to Co.Labs, Saskatchewan's first technology incubator. Co.Labs is devoted to providing early-stage technology startups and entrepreneurs with support and mentorship that help foster company growth and success.Saskatchewan is quickly becoming known for its tech startups, and Melanie helps inspire the notion - that anyone - including someone with little 'tech skills' but with great ideas - can find much support in Saskatchewan.On Civically Speaking we discuss how local tech sectors are critical to our civic fabric. Tune in to this show and be inspired. And, be sure to share this show with others. And, please be support community radio stations like cfcr.ca 90.5 FM that make shows like this possible.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.084 | 0.018 |
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; both teacher heads 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".