Ready, Steady Cook: The Growth of a Middle Aged Novice Researcher in the “Academic Kitchen”
Bibliographic record
Abstract
This paper takes inspiration from the work of Peter Elbow, who in his 1998 text Writing Without Teachers, described writing as a process of growing and cooking. As I read Elbow’s representations, I was struck by the similarity between learning to write and becoming a novice researcher. Elbow explained how each writer’s growth cycle would be individual and distinct. Thus, Elbow reasoned; “the main thing you must do if you want to help growing happen in your writing is to try to get a feel for the organic, developmental process” (pp. 42-43). Elbow explained that this means you need to get a sense of your development over space and time, and focus on “the shape of a set of changes occurring in a structure” (p. 43). Elbow described how if growing is the larger process, then cooking is the smaller…Cooking drives the engine that makes growing happen” (1998, p. 48). To me, this sounds like the feeling of being a graduate student and fledgling researcher. Since I commenced graduate study in 2009, I have struggled to make sense of my previous professional roles in relation to how I am developing my skills as a researcher. Life as a graduate student has been much like the process of writing as cooking that Elbow described. In this article, I tell the story of how these experiences have interlaced to represent one of the most profoundly important cooking and growing periods of my life.
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 | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.038 | 0.041 |
| Scholarly communication | 0.026 | 0.019 |
| Open science | 0.006 | 0.023 |
| Research integrity | 0.007 | 0.020 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".