Full and fractional output / impact counting

Attributes publication output (and citation impact) to entities using either full or fractional counting. Reviewers routinely ask for fractional counts so that a single multi-author / multi-institution paper is not counted in full for every contributor.

Usage

sm_count(
  corpus,
  method = c("full", "fractional"),
  level = c("institution", "author", "source"),
  call = rlang::caller_env()
)

Arguments

corpus: An sm_corpus.
method: "full" (default) gives each entity credit 1 per work it appears on. "fractional" splits each work's single unit of credit equally among the distinct entities on that work.
level: Entity level: "institution" (default), "author", or "source".
call: Caller environment for error reporting.

Value

A tibble, one row per entity, sorted by credit descending:

entity_id: Entity identifier.
entity_name: Human-readable name (falls back to entity_id).
n_works: Number of distinct works the entity appears on (this is the full count and is identical for both methods).
credit: Output credit: n_works under "full"; the sum of per-work fractional shares under "fractional".
weighted_citations: Citation impact attributed to the entity: summed cited_by_count under "full"; summed fractional-share weighted cited_by_count under "fractional".

Type-stable: an empty/inapplicable corpus returns a 0-row tibble with these columns.

Details

The fractional rule is the standard equal-share rule: a work with \(k\) distinct entities contributes \(1/k\) of credit (and \(1/k\) of its citations) to each. For level = "source" each work has a single source, so fractional and full counts coincide.

Examples

corpus <- sm_example_corpus(n_works = 30, seed = 1)
sm_count(corpus, method = "fractional", level = "author")
#> # A tibble: 58 × 5
#>    entity_id  entity_name      n_works credit weighted_citations
#>    <chr>      <chr>              <int>  <dbl>              <dbl>
#>  1 A000000001 Fatima Liu            30  7.04              100.  
#>  2 A000000002 Fatima Andersson       5  0.910               7.33
#>  3 A000000015 Sarah Tanaka           3  0.811               9.39
#>  4 A000000020 Maria Smith            3  0.792              15.0 
#>  5 A000000025 Erik Wang              3  0.783               4.58
#>  6 A000000039 James Patel            4  0.761              10.0 
#>  7 A000000016 Mohammed Brown         3  0.7                 7.2 
#>  8 A000000050 Mei Santos             3  0.65               14.4 
#>  9 A000000006 Fatima Brown           3  0.644               3.42
#> 10 A000000040 Lars Garcia            2  0.625               2.88
#> # ℹ 48 more rows