Taken Out of Context: Hazards in the Interpretation of Written Assessment Comments
Bibliographic record
Abstract
PURPOSE: Written comments are increasingly valued for assessment; however, a culture of politeness and the conflation of assessment with feedback lead to ambiguity. Interpretation requires reading between the lines, which is untenable with large volumes of qualitative data. For computer analytics to help with interpreting comments, the factors influencing interpretation must be understood. METHOD: Using constructivist grounded theory, the authors interviewed 17 experienced internal medicine faculty at 4 institutions between March and July, 2017, asking them to interpret and comment on 2 sets of words: those that might be viewed as "red flags" (e.g., good, improving) and those that might be viewed as signaling feedback (e.g., should, try). Analysis focused on how participants ascribed meaning to words. RESULTS: Participants struggled to attach meaning to words presented acontextually. Four aspects of context were deemed necessary for interpretation: (1) the writer; (2) the intended and potential audiences; (3) the intended purpose(s) for the comments, including assessment, feedback, and the creation of a permanent record; and (4) the culture, including norms around assessment language. These contextual factors are not always apparent; readers must balance the inevitable need to interpret others' language with the potential hazards of second-guessing intent. CONCLUSIONS: Comments are written for a variety of intended purposes and audiences, sometimes simultaneously; this reality creates dilemmas for faculty attempting to interpret these comments, with or without computer assistance. Attention to context is essential to reduce interpretive uncertainty and ensure that written comments can achieve their potential to enhance both assessment and feedback.
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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 | 0.193 | 0.500 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.015 | 0.023 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".