DOI:10.1068/a38287 Technology and the architecture of markets: reconfiguring the Canadian equity market
Bibliographic record
Abstract
Abstract. This paper examines the relation between technical possibilities, liberal logics, and the concrete reconfiguration of markets. It focuses on the enrolling of innovations in communication and information technologies into the markets traditionally dominated by stock exchanges. With the development of capacities to trade on-screen, the power of incumbent market makers has been challenged as a less stable array of competing quasi-public and private marketplaces emerges. Developing a case study of the Toronto Stock Exchange, I argue that narrative emphasis on the performative power of sociotechnical innovations, the deterritorialisation of financial relations, and the erosion of state capacities needs qualification. A case is made for the importance of developing an understanding of: the spaces of encounter between emerging social technologies and property rights, rules of exchange, and structures of governance; and the interplay of orderings of different institutional composition and spatial reach in the reconfiguration of market architectures. Only then can a better grasp be gained of the evolving dynamics between making markets, the regulatory powers of the state, and their delimitations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.412 | 0.067 |
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".