Bibliographic record
Abstract
In the face of an injustice toward a child, what is the responsibility of an early childhood educator? What are the risks if we speak out? What are the risks if we remain silent, if we do nothing? “Courage, Hannah Arendt (1958/1998) suggests, which we often think of as a quality of the ‘hero,’ is already present in the willingness to act and speak, to insert oneself into the world and begin a story” (Berger, 2010, p. 73). “Lynne is conscientious in her work but she is shy.” I find it quite interesting that this comment/label written about me on my report card 52 years ago by my kindergarten teacher entered my thoughts out of somewhere while I was contemplating how to tell this particular story about taking risks in my practice as an early childhood educator, and how I let go of normal. If indeed I am normally shy, I most definitely let go of my personal and professional normal in a family drop-in program in which I am the only early childhood educator-facilitator. It happened one busy morning in front of parents, grandparents, care providers, and children from infancy to five years old. People had been engaged in relation with each other and the mostly unconventional materials in the intentionally unstructured, untimed, and nontraditionally run program that is situated within a room in an urban elementary school.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.802 | 0.639 |
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".