Bibliographic record
Abstract
Have you ever felt envious of how a presenter told a story or engaged their audience? Even more personally, have you ever experienced how they captivated their audience while teaching the same material you teach. Did you feel like perhaps you were not so engaging with your audience or your audience wasn’t so engaged with you? Perhaps you are looking for a fresh way to present old themes and concepts. Through spending time in this workshop, you will come away with a practiced plan in hand for your next teaching endeavor. The purpose of this workshop is to empower you during the 90 minutes we have together to understand the mindset, skillset and toolset for storytelling. After we get to know each other, we’ll spend time with a brief few minutes didactic followed by time to write, craft and share that element of your story. Each person will then identify at least a single teaching topic for the focus of their story. That will be their focus for the workshop. This will process will repeat for each storytelling skill. There will be didactic, but the focus is on storytelling refinement so when you leave the workshop you have a plan specific to a topic you teach. The workshop concludes with participants voluntarily sharing their stories and reflecting on this process.
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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.034 | 0.009 |
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".