Bibliographic record
Abstract
The recent adoption in the U.S.A. and Canada of the management approach to identify reportable segments places relevance of the disclosed segmental data as the overriding concern over comparability. This study investigates whether relevance and comparability are mutually exclusive or can be simultaneously achieved in segmental disclosure. It is explicitly recognized that both properties are a joint function of segment performance and segment identification, the performance–identification conundrum. By using a data set drawn from the U.K., a jurisdiction that explicitly allows directors’ discretion when identifying reportable segments, and a series of tests which remove performance differences, the potential impact of segment identification on the relevance/comparability issue is highlighted. The results of the tests reveal that for a significant portion of the sample the levels of both relevance and comparability are simultaneously low due to the segment identification choices made. These choices appear to match the possible outcomes of following the management approach to identification.By implication, the adoption of the management approach may lead to reduced comparability and relevance in some cases.
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How this classification was reachedexpand
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".