The Use of Crowdsourcing and Social Media in Accounting Research
Bibliographic record
Abstract
ABSTRACT In this study, we investigate the use of crowdsourcing websites in accounting research. Our analysis shows that the use of crowdsourcing in accounting research is relatively low, and these websites have been mainly used to collect data through surveys and for conducting experiments. Next, we compare and discuss papers related to crowdsourcing in the accounting area with research in computer science (CS) and information systems (IS), which are more advanced in using crowdsourcing websites. We then focus on Amazon Mechanical Turk as one of the most widely used crowdsourcing websites in academic research to investigate what type of tasks can be done through this platform. Based on our task analysis, one of the areas in accounting research that can benefit from crowdsourcing websites is research on social media content. Therefore, we then discuss how research in CS, IS, and crowdsourcing websites can help researchers improve their work on social media.
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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 | 0.050 | 0.161 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".