W3030918247 Peer-reviewed research AI × management and organizations

Managing by Data: Algorithmic Categories and Organizing

Cristina Alaimo · Jannis Kallinikos

Also recorded as: doi:10.1177/0170840620934062

Organization Studies · 2020-07-02

Recorded claims
2
coded from the inspected source
Evidence classes
1
crossref-api-metadata
Recorded limitations
4
stated, never hidden

From the hash-bound evidence releases

Recorded claims

Each claim states what the inspected source says, at the recorded location—bounded by its scope and graded by its confidence. Nothing here is a synthesis across works.

The abstract reports that data-based categories and algorithmic objects at an online platform become organizing filters that reconfigure organizational activities and social order.

confidence: high crossref-api-metadata apw-c1-obot-journals

Scope: abstract-level online-platform study

The abstract explains how algorithmically produced data categories can become organizational reality filters that reshape objects, categories, and social order.

confidence: high crossref-api-metadata apw-c1-obot-journals

Scope: abstract-level

Structured relationships

In-corpus citations

Only source-supplied cites relationships whose two endpoints are admitted are shown. Invocation evidence remains a separate relationship.

Boundaries

Limitations & independence

Recorded at coding time, carried with the work forever. A claim without its limits is not evidence.

Recorded limitations

  • Abstract-level evidence only; no full text inspected
  • Bound from an already-retained journal-query response containing multiple works; no post-audit refetch
  • Crossref metadata only; full text was not inspected.
  • The publisher surface was not queried after the lane’s quarantined dynamic-challenge response; no independent publisher-page verification was attempted.

Source independence

publisher-only