In Response to “In Praise of Makeshift Finishing”: On Makeshifting, Publishing, and Storytelling
Bibliographic record
Abstract
Shifting Livelihoodsknow how thorough a storyteller he is.Chock-full of vivid scenes of the Chocó goldfields in Colombia, the book reads like a subtle proposition in favour of the thrills and rewards of slow writing.It was the cumulative result, as Tubb recounts here, of his choice to spend the past few years revising his doctoral dissertation into a monograph, rather than committing himself to building a portfolio of publish-or-perish articles.Yet it is not an argument of slowness that Tubb is advancing in his petition for "makeshift finishing."In many ways, he is telling us the exact opposite: Do not overthink it; do not spend too much time perfecting the text; get the "something good enough" out there and have it interact with the world.His appeal to makeshifting is a call to align our publishing strategies with "a shorter, more imperfect, contingent, and temporary way of writing"; one in which our publications are not the end result of settled theories and steadfast strategizing, but unstable pieces that are formative of, and formed by, ideas that are always fluid and incomplete.Tubb's thought-provoking piece is categorically anthropological.Stop overplanning the big picture!Put your skill and industry in lockstep with the messy temporalities of ethnographic (and human) practice!To be sure, in his adoption of the makeshift, and attendant rejection of formulae, one hears echoes of previous anthropological critiques of formulistic planning-say, Ingold's (2020, 14) ruminations on "amateur rigour," or Scott's (1998) juxtaposition of state formation with mētis.Tubb's argument also brings to mind Jesse Jonkman 1
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.014 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.015 | 0.017 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".