Bibliographic record
Abstract
BackgroundWho is Dave?Dave Cormier of the University of Prince Edward Island in Canada is renowned for coining the term MOOC (Massive Open Online Course) and for developing the notion of rhizomatic learning.In January 2014, he facilitated a MOOC entitled 'Rhizomatic learning: the community is the curriculum' (popularly known as #rhizo14 -the hashtag used on twitter and facebook for it), in which we (Maha and Sarah) were participants.How was this interview conducted?Since Dave lives in Canada, Maha lives in Egypt, and Sarah lives in Scotland, this was not a traditional interview.Maha conducted this interview with Dave rhizomatically (see below for explanation of rhizomatic), starting on google docs, facebook and twitter.Additionally, Maha crowdsourced part of this interview by inviting anyone who was interested in asking questions to pose those questions on her blog (Bali, 2014b) or on twitter using the hashtag #askjpd, and Dave Cormier answered those questions on a live (and recorded) google hangout, co-facilitated by us (Maha and Sarah).The full list of crowdsourced questions asked of Dave is available at this Storify 1 , 1the full hangout recording is available here 2 ,2and the transcript is here 3 .3Whatfollows is an edited summary of the interview, focusing on particular aspects that we thought would be of interest to readers of the JPD.Because of the unorthodox way of conducting the interview, the summary does not follow the order the questions were asked of Dave, nor does it include his full response to each question.We try to explain most terms in the body of the text, but have also provided a glossary at the end of the article for reference. What is Rhizomatic Learning?Dave Cormier is reluctant to define rhizomatic learning in a concise format.In a tweet, he told us he has 'been very careful to never write a definition', and he now prefers to think of it as a story 4 .4KeithHamon, a #rhizo14 participant and someone who has engaged with the notion of Contact: bali
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.023 |
| Insufficient payload (model declined to judge) | 0.018 | 0.010 |
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".