Tactical mediatization and activist ageing: pressures, push-backs, and the story of RECAA
Bibliographic record
Abstract
This case study examines the incorporation of digital media technologies and practices into Respecting Elders: Communities Against elder Abuse (RECAA), an organization of activist elders. By studying RECAA’s specific transition and following the work of Michel de Certeau (1988), I distinguish between tactical mediatization and strategic mediatization. Organizations such as RECAA must negotiate with political, ideological, administrative, and economic agendas that exert pressure and provide incentives for organizations “to mediatize” in order to survive in the current Canadian context. ‘Tactical mediatization’ is used to understand RECAA’s very deliberate and considered response to these pressures. This distinction provides a framework for conceptualizing how activist organizations such as RECAA struggle to exert agency within meta-processes that place mounting and insistent pressure on the organization to incorporate digital media technologies into its mandate and system of values.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.034 | 0.045 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".