The Production of Value Opinions by Specialized Valuers: Practical Sense and the Enactment of Judgment*
Bibliographic record
Abstract
ABSTRACT This article explores how valuers construct value opinions. Although some implications of outsourcing valuation to valuers for accounting purposes have been highlighted in the literature, we add a valuer perspective complementing the accountant's standpoint in order to obtain a fuller picture of these ramifications. Drawing on 62 interviews, we study the role of judgment in valuation and find that judgment is largely tied with the sensemaking of valuers. By examining the valuation activities of art and real estate valuers, we show that these specialized valuers follow a similar valuation process which allows them to grasp how to go on with valuation. This four‐phase process starts and ends by situating the opinion in its specific valuation context and is centered on iterations between researching, analyzing, and relativizing comparable data that may be adjusted to connect with a sense of what the value should be. Overall, judgment is shown to be enacted based on a practical sense of conducting valuation that includes a “gut feeling” of what represents a plausible value. This study suggests that the valuer's input should be recognized as situated, sense‐contingent, and vulnerable to the underlying politics and risks tied to providing an opinion. Our results also point to stark differences between how valuers approach the use of judgment compared to how judgment is typically exercised by accounting specialists.
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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 | 0.036 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.028 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".