Bibliographic record
Abstract
Accounting for intellectual capital is increasingly recognised to be one of the most fascinating and potentially far‐reaching challenges facing the accountancy profession. A growing literature, encompassing theoretical, empirical and practical elements, is currently emerging as researchers and practitioners endeavour to account for the hidden value that the intellectual capital concept denotes, and its pivotal role in the value creation process. To date, many of the most instructive advances have emanated from Scandinavia, reflecting these societies' sustained interest in necessity of accounting for the worth of employees, arguably the principal progenitor of intellectual capital accounting. Reports from a number of Australian, Canadian and European enquiries have added to the momentum of the intellectual capital accounting project, whilst affirming its links with contemporary debates about the information society, intangibles, knowledge management and business reporting. This paper reports and discusses some of the findings of a recently completed field study of intellectual capital accounting developments in the UK, funded by one of the professional accountancy bodies. Drawing on a series of semi‐structured interviews, it documents how senior managers in six knowledge‐based organisations view intellectual capital and related developments, their evolving attempts to respond to the challenges these present, and their progress in measuring and reporting their performance in these areas.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 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".