I’ll Get There, It Better Be Worth the Trip: Understanding WhoWe Are as Language Practitioners
Bibliographic record
Abstract
There are many ways of approaching the theme of identities and the ways in which identity is formed through language and the discourses in which we participate. Everyone has something to say about identity with each individual taking up the notion in a different way. When I was first asked about writing this paper, I wondered whether I had anything of relevance that would speak to the work that language arts researchers are doing. Over the past several years my own research has been eclectic, embracing every-thing from knowledge dissemination through community building to early childhood literacy. I wondered what threads if any connect who I am now with who I once was as primarily a language arts practitioner. And, since a part of my persona is storyteller, in this paper I share with you a story about my professional trip and about the ways in which I have come to under-stand identity. In 1984 I wrote an article entitled Restoring Power to Teachers: The Impact of Whole Language. This paper appeared a year later in the journal Language
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 | 0.018 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.042 | 0.052 |
| Scholarly communication | 0.032 | 0.039 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".