Bibliographic record
Abstract
Understanding corporate governance from an academic, practical, legislative, and social perspective has never been more important given the increasingly internationalized and financialized global economy. The global financial crisis of 2008 and resultant recession painfully evidence what many scholars have argued throughout the last century: in market-based economies, the economic and the social spheres remain inseparable. Today, such economies rest on the backs of publicly-traded corporations and understanding how such corporations are governed is the goal of this research effort. I argue that corporate governance is not a set of characteristics to be measured, modelled, and packaged as prescriptive principles but rather it is a series of exercises in decision-making across space and time rife with idiosyncrasies. As such, I present a novel corporate governance research agenda which focuses on the two pillars of decision-making, namely the environmental contexts within which the decision-making processes are embedded and the networks of agents involved in such processes. In a globalized economic setting, both the environmental contexts and the networks of agents readily transcend multiple social, cultural, and geo-political boundaries—resultantly, the research agenda I present is one predicated on the geography of governance. I apply my research agenda to the Canadian setting in efforts to demonstrate the utility of this research agenda while providing a better understanding of the Canadian model of corporate governance. This research is the first to systematically investigate the Canadian model of corporate governance and concludes that, contrary to predominant assumptions, there is no single harmonious national model but rather a mosaic of thirteen distinct provincial and territorial models which are asymmetrically linked by means of market actors and interactions and which exists in a temporary balance of parochial and cosmopolitan forces.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".