Mcluhan, Energy exploitation and the overextensions of man | Mcluhan , Explotación energía y ampliaciones en exceso los de hombre
Bibliographic record
Abstract
We make use of McLuhan’s Laws of Media and his notion that our technologies are “the extensions of man” to understand ecological issues in general and global warming in particular. We examine the evolution of humankind’s exploitation of energy that have increased human wealth and well being. We identify the benefits and costs of tool making, the control of fire, agriculture, steam engines, internal combustion engines, electricity generation and nuclear power plants. Using McLuhan’s Laws of Media we show that an energy exploitation technology or medium, and hence an extension of man, when pushed to its extreme can flip into it opposite an ‘overextension of man.’ This is certainly the case with the environmental challenges facing our planet and the survival of the human race today. These include the storage of nuclear waste and the depletion of natural resources. It is the burning of fossil fuels giving rise to pollution and the greenhouse effect, which is most troubling as the build up of greenhouse gases could devolve into a runaway effect threatening the very existence of human habitation on this planet.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".