Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND \nThis research proposes that nonhuman agents (chatbots) can be modelled by poetic engagement. Currently conversational machine learning agents are programmed to act only on utilitarian terms (Clark, 2019). Humans ascribing human-like qualities to non-human entities is the cause of abundant misinterpretations and complexity of relations, a transgression exemplary in ‘Tay’ (2016): a chatbot whom on its release by Microsoft cast aspersions on social media. If humans and nonhumans are to communicate, how this could be achieved between such different creatures is yet to be resolved: this is the gap that the work occupies. This work is also in line with the work of Annie Dorsen which explores the intersection between algorithmic art and performance. \n\nCONTRIBUTION \n‘The Pirate Girls say...’ was a performance net artwork, accessed via a captive portal Wi-Fi featuring oceanic paraphernalia. The audience received an email that was composed as concrete poetry (reminiscent of mail art). They were then encouraged to reply to ‘mutinous sentiments’ and ‘masquerading pirates’ (chatbots). Intervening on knowledge production, this work demonstrates the capacity of performative conversational non-human/human actors, and proposes this as a new genre of performance art. \n\nSIGNIFICANCE \nThe work was selected by curator Dr. Jaime Tsai for ‘Caught Stealing,’ a group show at the National Art School Gallery. Mauro-Flude gave artist talk in the public programme. It was presented alongside works by Destiny Deacon, Fiona Hall, Soda_Jerk, Joan Ross and Linda Dement. The exhibition was reviewed in Arts Review, Sydney Morning Herald and Swiss Review art magazine. An earlier iteration was peer reviewed by Unlikely Journal for transdisciplinary enquiry and curated into an exhibition at EastBlok - a key cultural venue in Montreal.
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.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.100 | 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 it