Mapping the relationship between genres and tasks: A study of undergraduate engineers
Bibliographic record
Abstract
Abstract This paper presents a study that explores the genres, tasks, and the relationship between them in the context of undergraduate engineering education. We build upon previous research on the information behaviors of engineers, by focusing on undergraduates' self‐reported information use in order to understand how they interact with genres and perform tasks. We compiled and validated genre and task repertoires using an online questionnaire with 103 undergraduates. To analyze the responses, we employed exploratory data analysis techniques, including correspondence analysis and cluster analysis. We interpreted three latent dimensions of the genre–task relationship: disciplinary versus education (Dimension 1); classroom versus independent coursework (Dimension 2); and conceptual versus procedural knowledge (Dimension 3). The distinction between the educational function of genres and tasks that support teaching and learning and those that support the socialization of students into the discipline and profession accounted for the majority of the variance in the dataset. The use of genres across tasks revealed that respondents prefer proximal and accessible information, and that personal and less formal genres are central to the learning experience. Findings provide insights into how undergraduates navigate complex information environments and interact with genres and tasks in their coursework.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".