The Routledge Handbook of Accounting Information Systems
Bibliographic record
Abstract
The Routledge Handbook of Accounting Information Systems is a prestige reference work offering a comprehensive overview of the state of current knowledge and emerging scholarship in the discipline of AIS. The pace of technological-driven change is rapid, and this revised edition provides a deeper focus on the technical underpinnings and organisational consequences of accounting information systems. It has been updated to capture the changes in technology since the previous edition. It now includes chapters and scholarly thought on artificial intelligence, predictive analytics and data visualisation, among others. Contributions from an international cast of authors provide a balanced overview of established and developing themes, identifying issues and discussing relevant debates. The chapters are analytical and engaging. Many chapters include cases or examples, and some provide additional resources for readers. The chapters also provide a reflection on where the research agenda is likely to advance in the future. This is a complete and indispensable guide for students and researchers in accounting and accounting information systems, academics and students seeking convenient access to an unfamiliar area, as well as established researchers seeking a single repository on the current debates and literature in the field.
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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.019 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.093 | 0.065 |
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".