Bibliographic record
Abstract
Electronic Dance Music (EDM) is a catalyst for creative expression, from the solo dance form known as shuffling, to “Flow Arts” activities (forms of self-expression inducing a flow state) like poi, hula hooping, orbiting, and gloving. Gloving is a subcultural practice and artform that couples LED lights with dexterous finger movements. It is a method of expression for dance music enthusiasts (also known as ravers) and has become an important component of the EDM scene, particularly over the past decade. Glovers engage in “secondary” performances to live music (DJs) using complex techniques such as symbolism, word painting, and what the community refers to as “musicianship.” Performances are comprised mainly of improvisatory gestures and movements drawn from a large lexicon, known collectively as “concepts.” Learning the skill of gloving involves taking part in oral transmission, cyphering, community building activities (both online and in-person), and cultivating a gloving identity with an accompanying pseudonym. This monograph illuminates the lacuna in the discourse regarding the lack of attention given to Canadian rave culture within the field of Electronic Dance Music Culture (EDMC). It elucidates primarily the practices of gloving within the Toronto rave scene. Toronto has played an integral role in the history of gloving from its earliest roots in “liquiding,” a style of dance that originated at raves during the 1990s. Several glovers and liquid dancers active in the Toronto rave scene between 1990 and 2020 are informants for the work. The work’s methodologies draw from existing practices, pulling from several fields including musicology, sociology, and ethnography. The work itself takes the form of an autoethnographic study, rooted in the participatory approaches of journalists Hunter S. Thompson and Simon Reynolds. The format is unconventional, embracing casual language, audio-visual materials, participant observation methodology, fieldnotes and meta-reflections, interviews, and pictures, presented in a mosaic approach à la Marshall McLuhan. The work also lacks a traditional critique, preferring to infer through storytelling and descriptions by informants from within the scene itself. The author of the work offers a robust critique of the theoretical idealization of fieldwork in EDMC scholarship by purposefully utilizing fluid positionality as a defining quality. The overarching arguments are threefold and include advocating for the acknowledgment of Toronto as a city of importance in the global rave scene, Flow Arts as a pathway to self-actualization, and calling to action the implementation of “Fluid Positionality” as an optimal way to negotiate “the vibe” for more nuanced data collection.
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 | 0.000 | 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".