Critical perspectives on digital spaces in educational research : editorial
Bibliographic record
Abstract
Globally, the digital is encroaching, reformulating, and recreating spaces in contemporary society (Kalantzis-Cope & Gherab-Martin, 2010). This is so in South Africa with the various applications of the digital having their roots in different places and spaces. Historically, we can look back to the development of portable video technology in the late 1960s in Canada. Organised through the National FilmBoard of Canada, a group of filmmakers initiated a new approach to documentary film production, engaging communities themselves in the process of film making (Rusted, 2010). The Fogo method, as it came to be called (named after Fogo Island, Newfoundland, where it was first used), was part of the Challenge for Change/ Societe nouvelle program. It brought together government, filmmakers, activists, and communities to address poverty through documentary film production and distribution (Baker, Waugh & Winton, 2010). It is worth noting that the Fogo method of participatory video was transported to South Africa in the late 1970s (Cain, 2009). This work became a significant part of anti-apartheid activism, especially in the Eastern and Western Cape (pers. comm. Lou Haysom). As Cain (2003) writes in herdoctoral study of participatory video that focuses on housing, water, and sanitation in communities in Eastern Cape.
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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 | 0.036 | 0.078 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.012 | 0.032 |
| Scholarly communication | 0.030 | 0.016 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.031 | 0.025 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".