A Review of Recent Research on Individual‐Level Score Reports
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract As the primary interface between test developers and multiple educational stakeholders, score reports are a critical component to the success (or failure) of any assessment program. The purpose of this review is to document recent research on individual‐level score reporting to advance the research and practice of score reporting. We conducted a search for research studies published or presented between 2005 and 2015, examining 60 scholarly works for (1) the research focus, (2) stated or implied theoretical frameworks of communication, and (3) the characteristics of data sets employed in the studies. Results show that research on score properties, especially subscores, and score report design/layout are well‐represented in the literature base. The predominant approach to score reporting has been through a cybernetics tradition of communication. Data sets were often small or localized to a single context. We present example research questions from novel communication frameworks, and encourage our colleagues to adopt new roles in their relationships to stakeholders to advance score reporting research and practice.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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 it