“You mean I spent all day here and I’m not leaving with any lesson plans?” Reflections on planning and hosting #TreatyEdCamp
Bibliographic record
Abstract
When we first began to conceptualize #TreatyEdCamp, a free professional development event for and by teachers with a focus on Treaty Education, we wondered how to convince teachers to “give up” a Saturday to grapple with this often difficult and uncomfortable content. With this in mind, we billed the event by noting that teachers would “ leave this event with new resources, a better understanding of Treaty Education in practice, a network of people to rely on for support, and a greater understanding of the significance of Treaty to our work as educators and to the process of reconciliation.” In this paper, we reflect on the process of planning and hosting this event, now in its second year, engaging in particular with our own thought process in stressing the “resource” aspect of the event, teacher response to the day, discomforts and learnings encountered in the planning process, and, ultimately, the lingering resistances still evident amongst some participants (as in the titular statement), which parallel the “just tell me what to do about it” mentality with which anti-oppressive education content is often met.
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 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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.020 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.019 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".