Bibliographic record
Abstract
An Ecological Analysis of Music Making in Ensemble Rehearsals Linda T. Kaastra, (eudaimonia96@gmail.com) Individual Interdisciplinary Graduate Studies Program, University of British Columbia Keywords: musical interaction, music gesture, performance analysis. A representation like the above allows the analyst to zoom in on single events while maintaining an awareness of context. Introduction Coordination Devices This poster presents a method for conceptualizing domains of activity in “real world” music making. An extension of Herbert H. Clark’s (1996) “action” theory of language use, this poster demonstrates how music making can be viewed as an integrated layering of verbal and nonverbal activity, personal and public goals, and situational awareness. A sample ecological analysis of performed music drawn from real world rehearsal data demonstrates how single parameters of performance, e.g. gesture, can be understood as one of a number of coordination devices used in the process of negotiating musical understanding. Clark provides the following representation for identifying coordination devices: For two people, A and B, it is common ground that p if and only if: 1. A and B have some information that some basis b holds; 2. b indicates to A and B that A and B have information that b holds; 3. b indicates to A and B that p. This poster provides several variables for p and b to demonstrate the layering of coordination devices (e.g. score markings, body motions, performance conventions) in the Takemitsu rehearsal data. Event Structure in Rehearsal Clark discusses music making as a single activity such as “playing a duet” or “playing a string quartet.” He suggests that such musical activities are periodic, “synchronized mainly by a cadence or rhythm” (p. 82), whereas day to day activities such as “shaking hands, eating dinner, waving good-bye,” are largely aperiodic. Clark also defines musical activities as balanced; even though he acknowledges that one member of an ensemble can initiate an activity, he feels that once the performance begins, the participants engage as equals. This analysis presents a picture of musical activity that is alternately periodic and aperiodic, balanced, and unbalanced. Real World Rehearsal Data Data for this study includes video-taped recordings of nine rehearsals and one performance of a single ensemble preparing Toru Takemitsu’s Masque for Two Flutes. Data was collected according to the guidelines of grounded theory. The flutists consented to have all rehearsals of this piece recorded and to have their discussion and repetition analyzed. Sessions are labeled by date (e.g. 92905 is September 29, 2005). Finally, a preliminary analysis highlights how conventions of performance such as, “being visible with [x],” (where x could be drawn from any of the following: Intent for: breath, beat, articulation, dynamics, character) can be used to distinguish between autonomous and participatory actions (Clark p. 61). Ensemble A-and-B is doing joint action k if and only if: 0. the action k includes 1 and 2; 1. A intends to be doing A’s part of k and believes that 0. 2. B intends to be doing B’s part of k and believes that 0. In this case, both flutists intend to participate together and to “be visible” with their intentions in order to facilitate coordination. Findings To isolate activities within the rehearsal process, I borrow Clark’s representation for identifying sections and boundaries: Rather than fulfilling static measurable roles in music making, coordination devices shift in emphasis as the flutists become more familiar with the music. In the first phase, considerable emphasis is placed on the beat markings added to the score. Later, body motion is emphasized as a coordination device. Later still, the use of body motion appears expendable as the flutists respond to experimental changes in the performance environment. Acknowledgments: The author would like to thank Dr. Brian Fisher, Dr. Eric Vatikiotis-Bateson and the National Sciences and Engineering Research Council of Canada for assistance with this project. Entry: A and B go from not being in J to being in J Body: A and B are in J Exit: A and B go from being in J to not being in J Entries and exits have to be engineered for each joint activity (p. 36). References: Clark, H. H. (1996). Using language. Cambridge: Cambridge University Press.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".