Biocultural nation making: Biopolitics, cultural-territorial belonging, and national protected areas
Bibliographic record
Abstract
While the academic literature on biopolitics has investigated how the life of the population and its biological capacities have increasingly become the target of political concern and intervention largely at the scale of the nation, the literature on nations and nationalism has explored nations as cultural-territorial units including questions of their emergence, ongoing production, and impacts. What these share is a similar if not nearly identical object of analysis: the nation or national population. These, however, are realms of scholarly debate that have largely, and quite surprisingly, bypassed one another. This paper advances the concept of biocultural nation making to bridge these debates and illustrate that nation making is at once biological and cultural-territorial and that these are deeply intertwined. We ground this in the experience of Canadian national parks, highlighting how “natural” environments like national parks are key sites of biocultural, and increasingly neoliberal, national production. Here, state conservation organizations promote park visitation as a means of, first, enabling an active, healthy, and economically productive national population. Second, parks are promoted on the grounds that they enable the experience of distinctively Canadian landscapes and places of national inclusion especially as park visitorship is expanded to include nontraditional visitors including immigrants, urban communities, and the youth. Parks, in short, have become vehicles of biocultural, and increasingly neoliberal, nation making. While there are indeed affirmative aspects to this, we also highlight hidden exclusions tied to the embrace of neoliberal logic, the limits of multiculturalism, and the ongoing erasure of Indigenous communities.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.022 | 0.053 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".