What Every Client Wants? (Re)mapping the Trajectory of Client Expectations Research
Bibliographic record
Abstract
Since the late 1980s, scholars have sought to understand what it is that speakers, audience members or conference organisers want from the interpreters with whom they work. The aim of this paper is to provide a critical review of the work that has taken place to understand these expectations, with a view to fostering a greater understanding of both the expectations of clients and how these expectations could be explored in a more nuanced fashion. Unlike Kurz (2001), who chose to provide an author-centred summary, publications are examined in this paper in chronological order, allowing the historical development of this area of research to be clearly seen. This structure also draws attention to the relative reduction in the number of publications on client expectations published in the first decade of the current millennium. This paper gives possible reasons for this reduction in publication frequency, followed by a detailed exploration of how more recent publications in this area differ from those published in earlier periods. These differences, and most notably the move towards dividing expectations into different categories, representing stereotypical and event-specific requirements of interpreters, are presented as offering a valuable starting point for future research.
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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 | 0.027 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.017 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.022 | 0.020 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".