From “Masters in Our Own House” to “Free in Our Own House”: Thoughts on the Legacy of an Electoral Slogan in Quebec
Bibliographic record
Abstract
“Masters in our own house” (“Maîtres chez nous”). No electoral slogan is better known or cited more often in all of Quebec’s documented literature. This was the electoral slogan used by the Liberal Party of Quebec in 1962, during the campaign to nationalize hydroelectricity led by René Lévesque, Minister of Natural Resources in Jean Lesage’s “équipe du tonnerre.” Sixty years later, another slogan echoes the original one: “Free in our own house” (“Libres chez nous”). This was the electoral slogan used by Éric Duhaime’s Conservative Party of Quebec during the 2022 electoral campaign, marked by the coronavirus pandemic. Beyond the obvious nod to “Masters in our own house,” where one word replaces the other, how does “Free in our own house” resonate with or differ from its illustrious predecessor? Does the “our own” used in both electoral slogans refer to the same collective subject? In other words, is the desire to be “Masters in our own house” still relevant today, in the early 21st century? These are the questions we intend to answer in this research note.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.021 | 0.020 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".