Bibliographic record
Abstract
The authors would like to acknowledge Stacy Bird, Joshua Nelson, and the SORG research team at LeMoyne College for research assistance on this project.The idea of music as an emotionally stimulating medium is not new. Historically, music has been utilized for a variety of purposes worldwide and throughout cultures. In the recent past, music has been investigated and incorporated into various medical and psychological theories and interventions. Loewy (1997) looked at the role of music in assessing and attending to pain management. Courtnage (2000) developed a rationale for the use of live music in the Neonatal Intensive Care Unit to regulate body functions and as a non-invasive method of treatment. Shiraishi (1997) showed that a structured music listening protocol decreased depression-anxiety levels and increased self-esteem levels in a sample of multi-risk mothers. Intensive investigations into the root causes of this phenomenon have led to research looking into different types of responses to music. Chief among these is the association between music and emotional response.Emotional Response to MusicVarious studies have looked at emotional response to musical stimuli in general. In a study of the effects of music on mood and memory, Sousou (1997) concluded that music had a significant effect on mood. Krumhansl ( 1997) studied whether music elicits an emotional response in listeners (the emotivist position) or simply expresses emotions that listeners recognize in the music (the cognitivist position). Krumhansl's research concluded that the effects of music observed support the emotivist view of musical emotions. North and Hargreaves (1997) found that emotions expressed through music are closely associated with the pleasing aspects of that stimulus. They concluded that response to music and other stimuli interact because they are based on like dimensions. Miller and Strongman (2002) looked at the emotional effects of music on religious experience. They found that the positive mood of the participants increased significantly from directly before the service until directly after the music and worship portions of the service. Vaestfjaell (2002) reviewed a body of research that showed music could alter moods and emotions. Vaestfjaell looked at various Mood Induction Procedures (MIPs) that were specifically developed to aid in determining how mood influences the cognitive process. Many of these operate by musical mood induction. Vaestfjaell concluded that Musical Mood Induction Procedures (MMIPs) could induce positive, negative and neutral moods.Research has also looked at musical influence on emotion from a biological and developmental standpoint. Bartlett ( 1996) compiled an overview of physiological responses to music. Bartlett reported a collection of studies describing music's effect on heart/pulse rate, skin conductivity, respiration, blood pressure, muscular tension/motor activities, motor/postural response, skin temperature, blood volume, stomach contractions and biochemical response studies. Nyklicek, Thayer and Van Doornen (1997) looked at the cardiorespiratory differentiation of emotions induced musically. They were able to conclude that emotions could be categorized indicating a clear physiological differentiation. Blood, Zatorre, Bermudez and Evans (1999) used positron emission tomography (PET) to examine cerebral blood flow (CBF) changes related to affective responses to music. They found that CBF covariations existed in distinct paralimbic and neocortical regions as a function of the perceived pleasantness or unpleasantness of the music. Blood, Zatorre, Bermudez and Evans conclude it is possible that music uses neural mechanisms similar to those associated with pleasant and unpleasant emotional states. Panksepp and Bernatzky (2002) described the potential role of the brain's emotional systems in music appreciation citing examples of how music may promote behavioral changes including effects on memory, mood, brain activity and autonomie responses. …
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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 | 0.001 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".