MCLACHLAN, SARAH (28 JAN 1968–)
Bibliographic record
Abstract
Best known as the founder of Lilith Fair, a tour providing a forum for female artists, Sarah McLachlan emerged from the 1990s as one of the highly regarded-and commercially successful-singer/songwriters on the music scene. Both her recordings and Lilith Fair have encouraged countless women to consider popular music as a viable career option. Born in Halifax, Nova Scotia, McLachlan took piano, guitar, and voice lessons as a youth. Attracting the interest of the Nettwerk label while performing with the new wave act, October Game, she recorded Touch (Nettwerk 30024; 1988; released in the U.S. by Arista #18594; 1989; #132), which went gold in Canada. The follow-up, Solace (Arista #18631; 1991; #167), showed McLachlan to be treading water. However, the next LP, Fumbling Towards Ecstasy (Arista #18725; 1993; #50), with its insightful song lyrics-most notably, adding a sociopolitical dimension to her prior concentration on personal relationships-complementing her expressive singing, represented an artistic breakthrough. The momentum generated by its triple platinum sales extended to the next album of newly recorded material, Surfacing (Arista #18970; 1997; #2), which ultimately sold more than 7 million copies on the strength of three hit singles: “Adia” (Arista #13497; 1998; #3), “Angel” (Arista #13497/13621; 1997; #4), and “Building a Mystery” (Arista #13395; 1997; #13). The LP also won Grammy awards for Best Female Pop Vocal and Best Pop Instrumental Performance.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.236 | 0.099 |
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".