Financial Reporting Interview-Based Research: A Field Research Primer with an Illustrative Example
Bibliographic record
Abstract
ABSTRACT To better focus financial reporting research on key issues as seen by participants in the financial reporting process and to give added depth to the interpretation of archival and experimental results, there have been increased calls for financial reporting researchers to “enter the field.” As field research methods, especially interview-based, are rarely covered in accounting doctoral programs that focus on archival or experimental research, the goal of this article is to provide a basic primer on how to conduct positivist field-based research using qualitative interview methods. We assemble a set of resources that facilitate the transfer of knowledge about the interview method, both by reviewing the explicit knowledge that needs to be acquired, as well as by illustrating how we carried out a study on the earnings press release creation process. Such a “how to do” approach is well suited for the passing on of the tacit knowledge required by researchers beginning a qualitative research program. We hope to aid novice field researchers in financial reporting gain a basic fluency in qualitative interview-based methods. Increased fluency in the production of valid, reliable, field-based financial reporting research will benefit the financial reporting research community as a whole by leading to a greater appreciation of how financial reporting process participants see the world they are active in.
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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 | 0.081 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".