Bibliographic record
Abstract
What is an apocalypse anyways? Most consider it the end of the world, but as April Anson (2017) points out the word ‘apocalypse’ is derived from the Greek term “apokalypsis” meaning “something revealed or uncovered”. But what if apocalypse accounted for these histories and revealed something more than “an end”? What if the apocalypse signalled hope that there will be a future? By drawing on Indigenous knowledge, Skawennati’s machinima She Falls for Ages (2017) re-frames apocalypse as the beginning of time, rejecting the narratives in science fiction or climate-fiction (cli-fi) of apocalypse as impending doom. Skawennati is a Mohawk futurist based in Tiohtiá:ke/Montreal, best known for her machinima creations—movies typically produced using video-game software—and digital media works that re-interpret and connect historical events to the present and future. She Falls for Ages (2017), is a re-telling of the Haudenosaunee creation story, where Sky Woman falls from Sky World through a hole in the sky and lands on a turtle’s back, both eventually becoming Turtle Island, North America as we know it today. Creation stories like the one of Sky Woman can often take days to tell, but Skawennati uses the Second-life video game platform to show the futurist place where Sky Woman falls from as one that is dying or, in many ways, experiencing apocalypse. Skawennati’s version of the apocalypse is stark difference from cli-fis such as The Day After Tomorrow (2002), that appropriate Judeo-Christian biblical narratives of the apocalypse and erases the existence of BIPOC perspectives. I consider Skawennati’s machinima as a reconceptualization of apocalypse that moves away from narratives that insist on White-settler survival and settler-colonial ideas about apocalypse and the anthropocene. As the author of this text is a woman of color and settler scholar, she foregrounds Indigenous scholars’ research about the anthropocene to analyze this work.
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) | 0.000 | 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".