Understanding the key Characteristics of Consumer Behaviour-Post Covid Scenario
Bibliographic record
Abstract
The COVID-19 pandemic is changing our lives in multiple ways —ways that we, as a society, will come to detect, understand, and accept many years from now. A critical situation pushes human behaviour towards different directions with some aspects of behaviour being irrevocable. COVID-19 pandemic is not a normal crisis, and to control the spread of disease various measures were taken including complete and then partial lockdown. Since all elements of the economy are intricately interrelated with public health measures and lockdown, this resulted in economic instabilities of the nations hinting towards change in market dynamics. In every market,consumers are the drivers of the market competitiveness, growth and economic integration. With economic instability, consumers are also experiencing a transformation in behaviour, though how much of transformation experienced during the crisis will sustain is a question. This chapter looks at the consumer behaviour during COVID-19 crisis and in the subsequent lockdown period when the world stood still for more than a quarter of a year.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".