A Folk Approach to Imaginary Media
Bibliographic record
Abstract
In both the documents of the mid-century American folk revival and the tactical media ‘movement’, distinctions are consistently eroded between expert and amateur, theory and practice. For both Pete Seeger and (for instance) Geert Lovink, the point of singing or writing is so that others might in turn put the art and ideas to work—into their own concerts, songs, interventions, or disturbances (a process that might happen quickly or take a long time). There is thus an energetic and DIY hastiness to issues of Sing Out! and Broadside , magazines that published songs with an eye to their utility for the voices of their readers; we see this open spirit of generosity in the works of Lovink and the Critical Art Ensemble as well, the latter of which included instructions on Game Boy hacking in one of their books. Of course, a ‘tinkerer’ impulse has also been evident in media-archaeological approaches to media history, wherein the generation of artworks and/or impossible technologies has been considered as intellectually valuable an endeavor as scholarly publication. While Media Artist in Residence at the University of New Brunswick in Fredericton, Canada (20142015), I attempted to allow these apparently divergent but in fact fellow-travelling tributaries to converge into a single organization, which I called ‘The New Brunswick Laboratory of Imaginary Media Research + Design’. Inspired by the Hootenannies and sing-alongs but also by the distributed-in-solidarity networks, databases, and writing machines generated by this complex tradition, my intention was to map the creative utopianism of the long American folk revival more directly onto the problematics of media-archaeological design, in a perhaps tactical way. Instead of swapping songs, or writing topical ballads, what if we got together and sang new channels? Might the collaborative act of discussing or sketching impossible communication technologies be conceived as a tactical media manoeuvre? Would we need to cover new territory, like solitary artists and scholars are obligated to do, or could we just find value in the ‘singing’ together itself? As Jussi Parikka observes, the laboratory is a site of imagining that ‘[shifts] the coordinates of what is possible’. Several scholars of imaginary media history have similarly looked to the design of communication technologies (which all, at one point in time, were mere fictions) and the relationship between such designs and sociocultural pasts, presents, and futures.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.013 | 0.074 |
| Scholarly communication | 0.028 | 0.011 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".