Bibliographic record
Abstract
Whenever a decision is made in a social, political, or economic context, it is implicitly grounded in an ethical outlook. But where do these outlooks come from? To investigate this query, I examine the basis for ethical decisions regarding technology, focusing specifically on geoengineering responses to climate change. Subsequently, I argue that ethical considerations concerning climate change, and their corresponding practical decisions, cannot be reliably made without sufficient intelligibility regarding the objects and entities these decisions pertain to. To achieve this, I employ a Heideggerian phenomenological framework through which being affords intelligibility. Doing so elucidates fundamental inconsistencies in the way humans interact with technology. We are caught up in what Heidegger calls enframing, the representation of beings as energy reserves. This is the ground on which our ethical claims are based, but representation cannot afford actuality. When things are represented in this way, truth is set aside in favour of will, and intelligibility is lost. The goal, then—if we wish our ethical decisions to be legitimate—must be to gain intelligibility. We must therefore free ourselves from enframing and look toward being. We cannot, as Heidegger says, affect enframing’s removal, but we can prepare ourselves for such a change. Only once this change occurs, can our relationship to technology be intelligible.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.044 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".