Segment Information: What Do European Small and Mid-Caps Disclose?
Bibliographic record
Abstract
The adoption of the IFRS 8 accounting standard symbolises the IASB's dual commitment: an effort toward convergence and harmonisation with the American standard, but also an effort to optimise the standardisation process through an unprecedented study: a post-implementation review. Many studies have laid the groundwork for an implementation review of the standard, mostly focusing on large firms. However, intermediate-size companies – which are much more numerous – are also faced with the application of IFRS standards. In this context, our study aims to analyse the implementation of IFRS 8 by a sample of intermediate-size European listed companies. Our research questions mainly focus on issues of compliance with the standard and the comparability of segment information reported by intermediate-size European companies. Our findings reveal a lower level of compliance than that observed in previous studies on samples of multinationals. The intermediate-size European companies in our sample use fewer segments and provide less information per segment, without however neglecting voluntary disclosures. Some significant differences emerge between companies depending on their country of domicile and their economic sector.
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 | 0.011 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".