Reconsidering trial and error: A central information practice in everyday food life
Bibliographic record
Abstract
This poster reports selected findings from an interpretivist qualitative study of the everyday food lives of people living in urban and rural Canada. This research sought to illuminate how people come to feel informed about food, how people navigate food information on ordinary and extraordinary days, and how people’s encounters with food information are embodied. Through constructivist grounded theory analysis of data resulting from interviews and video tours, this research identified areas of information practice held in common across a diverse group of participants. This poster focuses on one information practice, trial and error, which emerged as complex and generative. The terminology of “trial and error” originally referred to a form of learning that hinges on repetition, with learners trying again and again to solve problems correctly. In this research, participants’ trial and error processes were richer than this. Their processes were also more sophisticated than the portrayal of trial and error in information science scholarship, which tends to emphasize finite processes of overcoming failure, rather than open-ended processes of exploration and experimentation. Trial and error in people’s food lives is an iterative, embodied, information-generating cycle. The result of each effort—each seasoning-to-taste, recipe selection, or dietary adjustment—informs the next effort. By shedding light on trial and error, this poster advances information practices theory in the context of everyday life. It also questions dichotomies that position more widely valorized modes of information engagement, such as critical thinking, as unique in their sophistication.
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 | 0.051 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.017 | 0.116 |
| Scholarly communication | 0.019 | 0.021 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".