The everyday construction of value: A Canadian investment fund, Chilean water infrastructure, and financial subordination
Bibliographic record
Abstract
Abstract Infrastructure in several economies in the Global South has rapidly undergone financialization, aided and abetted by governments opening-up their infrastructure assets to global institutional investors in search of stable, predictable revenue streams. This account of financialization could be the end of the story were it not for the fact that Christophers (2015) and others have shown that institutional investors are not simply in the game of ‘finding’ value or ‘harvesting it’ from obliging states, rather they actively construct it. What often catches the eye, however, are the more overt forms of financial engineering (Ashton et al., 2012), whereas what tends to go unnoticed are the ways in which infrastructure assets are routinely ‘worked’ to generate value over time. Here, we draw attention to a slower-paced financialization of infrastructure assets where, following Chiapello (2015, 2020), investors are engaged in a continual process of evaluation and revaluation of their assets to add value over and above prevailing benchmarks. Taking the example of Canada's Ontario Teachers’ Pension Plan (OTPP) and its extensive investments in Chilean water infrastructure, this article considers how a global investment fund draws on financial practices developed in the advanced economies to add value to long term infrastructure assets in the Global South. Such practices, we argue, enact a routine form of financial subordination which does not match the familiar image of wholly subservient and dominated dependent economies. Rather, the power asymmetries involved equate less to a zero-sum game and more to a game where the benefits are unequally shared between asset managers in the Global North and states in the Global South, where effectively the latter cooperate in their own submission in ways that are not always acknowledged as such.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".