Bibliographic record
Abstract
This paper examines the relationship between digital spaces and the Land, proposing a framework that maps the origins of digital space to contextualize our relationships with lithium. Lithium, in the form of batteries, enables us to work remotely and online, birthing the possibility of different technologies while also tying said technologies to mining and extraction. Through this lens of layered relationships, the paper interrogates the metaphors and technological languages used to disconnect digital spaces from the Land and obscure the settler-colonial practices of extraction underpinning them. By tracing my own relationships to the Lands and waters destroyed to produce the batteries in my computer, I aim to reimagine these connections and offer an alternative perspective. Rather than presenting a definitive solution, this paper advocates for using metaphors against colonial structures to envision other worlds where digital spaces are recognized as rooted in both Land and kinship relations. The conclusions are that Data is sacred and kin; Data is born from the Land; Land connects multiple worlds; and digital spaces are Land. These conclusions aim to re-think settler-colonial extraction by recognizing the sacred interconnections between Land, digital spaces, and the materials that power our technologies.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.027 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".