‘Let’s find out’: the historian laureate and participatory heritage discovery
Bibliographic record
Abstract
Developing modes of engagement in the construction of public heritage knowledge emphasize participatory media and active collaboration with citizens. The City of Edmonton created the first historian laureate position in Canada in 2010, and related programs are still rare. This article considers the interaction of narrative content with social and technological contexts of production, viewing the role of the historian laureate as amateur historian and professional storyteller. The historian laureate operates primarily in accessible contexts of leisure, mediated in part through digital technologies, and can respond relatively directly to community interests as a heritage coordinator rather than expert. Rather than representing oppositional or disruptive power to official heritage discourses, the project enables the production of ‘small heritages’ through a series of story episodes. These stories focus on events, people, places and artifacts that typically fall outside the meta-narratives and monuments of a city’s heritage landscape. The historian laureate, embodying or articulating local experience in ways amenable to leisure activity, demonstrates capacities to produce largely indeterminate, diverse and porous ideas of place and histories as part of a bottom-up social generation of knowledge.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.024 | 0.062 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".