Examining Technology Enactment in the Fixed Income Market
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
While the use of electronic trading systems (ETS) in fixed income (FI) market is becoming more common in the last few years, only a small number of empirical studies have been conducted to examine their adoption and impact in the market. In this study, we use the technology enactment framework (TEF) to identify various institutional and organizational factors that influence ETS adoption. Based on a comprehensive survey and face-to- face interviews with senior managers, sales representatives, and traders from major Canadian financial institutions, we find that the FI market is characterized by a high level of embedded relationships and that the ETS users do not entirely trust their ETS. This study also presents, among others, important findings regarding the impact of ETS on the users' job performance and their perception about the asymmetric nature of benefits provided by ETS in the FI market.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.009 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it