Translation and validation of the Graham‐Harvey survey for the Brazilian context
Bibliographic record
Abstract
Purpose The purpose of this paper is to report on the systematic translation and content validation method used to produce the Brazilian Portuguese version of the Duke Special Survey on Corporate Policy by Graham and Harvey. Design/methodology/approach In accordance with the requirements for cross‐cultural application of surveys, the paper accounts for obvious differences in language, culture, and the institutional setting and employ well‐known techniques from the field of psychology, such as the use of backtranslation, to ensure faithfulness to the original survey. A panel of experts served as judges in evaluating the clarity of language and the practical pertinence and theoretical dimensions of the questionnaire. Coefficients of content validity for each item and for the instrument as a whole are reported. Findings The results illustrate how a questionnaire designed for one country should be rigorously translated and validated prior to use in another country. Research limitations/implications Although the content validity of the translated version of the Duke Special Survey on Corporate Policy for use in Brazil is generally satisfactory, a few items may prove to be a challenge for the Brazilian CFO to answer, particularly those questions concerning features that are uncommon in the Brazilian financial market. Originality/value This paper explores the field study method in finance by borrowing from the vast experience of psychology research in the rigorous translation and validation of survey instruments. This study also highlights the similarities and differences in the interpretation of questions between emerging and developed markets.
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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 | 0.030 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".